Extreme returns and tail modelling of the Nikkei 225 index for the Japanese equity market

November 23, 2023 by Shengyu ZHENG

Extreme returns and tail modelling of the Nikkei 225 index for the Japanese equity market

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) describes the statistical behavior of extreme returns of the Nikkei 225 index for the Japanese equity market and explains how extreme value theory can be used to model the tails of its distribution.

The Nikkei 225 index for the Japanese equity market

The Nikkei 225, often simply referred to as the Nikkei, is a stock market index representing the performance of 225 major companies listed on the Tokyo Stock Exchange (TSE). Originating in 1950, this index has become a symbol of Japan’s economic prowess and serves as a crucial benchmark in the Asian financial markets. Comprising companies across diverse sectors such as technology, automotive, finance, and manufacturing, the Nikkei 225 offers a comprehensive snapshot of the Japanese economic landscape, reflecting the nation’s technological innovation, industrial strength, and global economic influence.

Utilizing a price-weighted methodology, the Nikkei 225 calculates its value based on stock prices rather than market capitalization, distinguishing it from many other indices. This approach means that higher-priced stocks have a more significant impact on the index’s movements. Investors and financial analysts worldwide closely monitor the Nikkei 225 for insights into Japan’s economic trends, market sentiment, and investment opportunities. As a vital indicator of the direction of the Japanese stock market, the Nikkei 225 continues to be a key reference point for making informed investment decisions and navigating the complexities of the global financial landscape.

In this article, we focus on the Nikkei 225 index of the timeframe from April 1st, 2015, to April 1st, 2023. Here we have a line chart depicting the evolution of the index level of this period.

Figure 1 below gives the evolution of the Nikkei 225 index from April 1, 2015 to April 1, 2023 on a daily basis.

Figure 1. Evolution of the Nikkei 225 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the daily logarithmic returns of Nikkei 225 index from April 1, 2015 to April 1, 2023 on a daily basis. We observe concentration of volatility reflecting large price fluctuations in both directions (up and down movements). This alternation of periods of low and high volatility is well modeled by ARCH models.

Figure 2. Evolution of the Nikkei 225 index logarithmic returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Nikkei index

Table 1 below presents the summary statistics estimated for the Nikkei 225 index:

Table 1. Summary statistics for the Nikkei 225 index.
summary statistics of the Nikkei 225 index returns
Source: computation by the author (data: Yahoo! Finance website).

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively. We can conclude that during this timeframe, the Nikkei 225 index takes on a slight upward trend, with relatively important daily deviation, negative skewness and excess of kurtosis.

Tables 2 and 3 below present the top 10 negative daily returns and top 10 positive daily returns for the index over the period from April 1, 2015 to April 1, 2023.

Table 2. Top 10 negative daily returns for the Nikkei 225 index.
Top 10 negative returns of the Nikkei 225 index
Source: computation by the author (data: Yahoo! Finance website).

Table 3. Top 10 positive daily returns for the Nikkei 225 index.
Top 10 positive returns of the Nikkei 225 index
Source: computation by the author (data: Yahoo! Finance website).

Modelling of the tails

Here the tail modelling is conducted based on the Peak-over-Threshold (POT) approach which corresponds to a Generalized Pareto Distribution (GPD). Let’s recall the theoretical background of this approach.

The POT approach takes into account all data entries above a designated high threshold u. The threshold exceedances could be fitted into a generalized Pareto distribution:

Illustration of the POT approach

An important issue for the POT-GPD approach is the threshold selection. An optimal threshold level can be derived by calibrating the tradeoff between bias and inefficiency. There exist several approaches to address this problematic, including a Monte Carlo simulation method inspired by the work of Jansen and de Vries (1991). In this article, to fit the GPD, we use the 2.5% quantile for the modelling of the negative tail and the 97.5% quantile for that of the positive tail.

Based on the POT-GPD approach with a fixed threshold selection, we arrive at the following modelling results for the GPD for negative extreme returns (Table 4) and positive extreme returns (Table 5) for the Nikkei 225 index:

Table 4. Estimate of the parameters of the GPD for negative daily returns for the Nikkei 225 index.
Modelling of negative extreme returns of the Nikkei 225 index
Source: computation by the author (data: Yahoo! Finance website).

Table 5. Estimate of the parameters of the GPD for positive daily returns for the Nikkei 225 index.
Modelling of positive extreme returns of the Nikkei 225 index
Source: computation by the author (data: Yahoo! Finance website).

Figure 3. GPD for the left tail of the Nikkei 225 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Figure 4. GPD for the right tail of the Nikkei 225 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Applications in risk management

Extreme Value Theory (EVT) as a statistical approach is used to analyze the tails of a distribution, focusing on extreme events or rare occurrences. EVT can be applied to various risk management techniques, including Value at Risk (VaR), Expected Shortfall (ES), and stress testing, to provide a more comprehensive understanding of extreme risks in financial markets.

Why should I be interested in this post?

Extreme Value Theory is a useful tool to model the tails of the evolution of a financial instrument. In the ever-evolving landscape of financial markets, being able to grasp the concept of EVT presents a unique edge to students who aspire to become an investment or risk manager. It not only provides a deeper insight into the dynamics of equity markets but also equips them with a practical skill set essential for risk analysis. By exploring how EVT refines risk measures like Value at Risk (VaR) and Expected Shortfall (ES) and its role in stress testing, students gain a valuable perspective on how financial institutions navigate during extreme events. In a world where financial crises and market volatility are recurrent, this post opens the door to a powerful analytical framework that contributes to informed decisions and financial stability.

Download R file to model extreme behavior of the index

You can find below an R file (file with txt format) to study extreme returns and model the distribution tails for the Nikkei 225 index.

Useful resources

Academic resources

Embrechts P., C. Klüppelberg and T. Mikosch (1997) Modelling Extremal Events for Insurance and Finance Springer-Verlag.

Embrechts P., R. Frey, McNeil A.J. (2022) Quantitative Risk Management Princeton University Press.

Gumbel, E. J. (1958) Statistics of extremes New York: Columbia University Press.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Other resources

Extreme Events in Finance

Chan S. Statistical tools for extreme value analysis

Rieder H. E. (2014) Extreme Value Theory: A primer (slides).

About the author

The article was written in November 2023 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).

Extreme returns and tail modelling of the FTSE 100 index for the UK equity market

November 23, 2023 by Shengyu ZHENG

Extreme returns and tail modelling of the FTSE 100 index for the UK equity market

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) describes the statistical behavior of extreme returns of the FTSE 100 index for the UK equity market and explains how extreme value theory can be used to model the tails of its distribution.

The FTSE 100 index for the UK equity market

The FTSE 100 index, an acronym for the Financial Times Stock Exchange 100 Index, stands as a cornerstone of the UK financial landscape. Comprising the largest and most robust companies listed on the London Stock Exchange (LSE), this index is a barometer for the overall health and trajectory of the British stock market. Spanning diverse sectors such as finance, energy, healthcare, and consumer goods, the FTSE 100 encapsulates the economic pulse of the nation. The 100 companies in the index are chosen based on their market capitalization, with larger entities carrying more weight in the index’s calculation, making it a valuable tool for investors seeking a comprehensive snapshot of the UK’s economic performance.

Investors and analysts globally turn to the FTSE 100 for insights into market trends and economic stability in the UK. The index’s movements provide a useful reference point for decision-making, enabling investors to gauge the relative strength and weaknesses of different industries and the economy at large. Moreover, the FTSE 100 serves as a powerful benchmark for numerous financial instruments, including mutual funds, exchange-traded funds (ETFs), and other investment products. As a result, the index plays a pivotal role in shaping investment strategies and fostering a deeper understanding of the intricate dynamics that drive the British financial markets.

In this article, we focus on the FTSE 100 index of the timeframe from April 1st, 2015, to April 1st, 2023. Here we have a line chart depicting the evolution of the index level of this period.

Figure 1 below gives the evolution of the FTSE 100 index from April 1, 2015 to April 1, 2023 on a daily basis.

Figure 1. Evolution of the FTSE 100 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the daily logarithmic returns of FTSE 100 index from April 1, 2015 to April 1, 2023. We observe concentration of volatility reflecting large price fluctuations in both directions (up and down movements). This alternation of periods of low and high volatility is well modeled by ARCH models.

Figure 2. Evolution of the FTSE 100 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the FTSE 100 index

Table 1 below presents the summary statistics estimated for the FTSE 100 index:

Table 1. Summary statistics for the FTSE 100 index returns.
Summary statistics of the FTSE 100 index returns
Source: computation by the author (data: Yahoo! Finance website).

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively. We can conclude that during this timeframe, the FTSE 100 index takes on a slight upward trend, with relatively important daily deviation, negative skewness and excess of kurtosis.

Tables 2 and 3 below present the top 10 negative daily returns and top 10 positive daily returns for the index over the period from April 1, 2015 to April 1, 2023.

Table 2. Top 10 negative daily returns for the FTSE 100 index.
Top 10 negative returns of the FTSE 100 index
Source: computation by the author (data: Yahoo! Finance website).

Table 3. Top 10 positive daily returns for the FTSE 100 index.
Top 10 positive returns of the FTSE 100 index
Source: computation by the author (data: Yahoo! Finance website).

Modelling of the tails

The POT approach takes into account all data entries above a designated high threshold u. The threshold exceedances could be fitted into a generalized Pareto distribution:

Illustration of the POT approach

Table 4. Estimate of the parameters of the GPD for negative daily returns for the FTSE 100 index.

Source: computation by the author (data: Yahoo! Finance website).

Table 5. Estimate of the parameters of the GPD for positive daily returns for the FTSE 100 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 3. GPD for the left tail of the FTSE 100 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Figure 4. GPD for the right tail of the FTSE 100 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Applications in risk management

Why should I be interested in this post?

Download R file to model extreme behavior of the index

You can find below an R file (file with txt format) to study extreme returns and model the distribution tails for the FTSE 100 index.

Useful resources

Academic resources

Embrechts P., C. Klüppelberg and T. Mikosch (1997) Modelling Extremal Events for Insurance and Finance Springer-Verlag.

Embrechts P., R. Frey, McNeil A.J. (2022) Quantitative Risk Management Princeton University Press.

Gumbel, E. J. (1958) Statistics of extremes New York: Columbia University Press.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Other resources

Extreme Events in Finance

Chan S. Statistical tools for extreme value analysis

Rieder H. E. (2014) Extreme Value Theory: A primer (slides).

About the author

The article was written in November 2023 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).

Copula

January 27, 2024November 23, 2023 by Shengyu ZHENG

Copula

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) presents copula, a statistical tool that is commonly used to model dependency of random variables.

Linear correlation

In the world stacked with various risks, a simplistic look of individual risks does not suffice, since the interactions between risks could add to or diminish the aggregate risk loading. As we often see in statistical modelling, linear correlation, as one of the simplest ways to look at dependency between random variables, is commonly used for this purpose.

Definition of linear correlation

To put it concisely, the linear correlation coefficient, denoted by ‘ρ(X,Y)’, takes values within the range of -1 to 1 and represents the linear correlation of two random variables X and Y. A positive ‘ρ(X,Y)’ indicates a positive linear relationship, signifying that as one variable increases, the other tends to increase as well. Conversely, a negative ‘ρ(X,Y)’ denotes a negative linear relationship, signifying that as one variable increases, the other tends to decrease. A correlation coefficient near zero implies a lack of linear relation.

Limitation of linear correlation

As a simplistic model, while having the advantage of easy application, linear correlation fails to capture the intricacy of the dependance structure between random variables. There exist three main limitations of linear correlation.

ρ(X,Y) only gives a scalar summary of linear dependence and it requires that both var(X) and var(Y) must exist and finite;
Given that assumption that X and Y are stochastically independent, it can be inferred that ρ(X,Y) = 0. Whereas, the converse does not stand for most of the cases (except if (X,Y) is a Gaussian random vector).
Linear correlation is not invariant with regard to strict increasing transformations. If T is such a transformation, ρ(T(X),T(Y)) ≠ ρ(X,Y)

Therefore, if we have in hand the marginal distributions of two random variables and their linear correlations, it does not suffice to determine the joint distribution.

Copula

A copula is a mathematical function that describes the dependence structure between multiple random variables, irrespective of their marginal distributions. It describes the interdependency that transcends linear relationships. Copulas are employed to model the joint distribution of variables by separating the marginal distributions from the dependence structure, allowing for a more flexible and comprehensive analysis of multivariate data. Essentially, copulas serve as a bridge between the individual distributions of variables and their joint distribution, enabling the characterization of their interdependence.

Definition of copula

A copula, denoted typically as C∶[0,1]^d→[0,1] , is a multivariate distribution function whose marginals are uniformly distributed on the unit interval. The parameter d is the number of variables. For a set of random variables U₁, …, U_d with cumulative distribution functions F₁, …, F_d, the copula function C satisfies:

C(F₁(u₁),…,F_d(u_d)) = ℙ(U₁≤u₁,…,U_d≤u_d)

Fréchet-Hoeffding bounds

The Fréchet–Hoeffding theorem states that copulas follow the bounds:

max{1 – d + ∑^d_i=1u_i} ≤ C(u) ≤ min{u₁, …, u_d}

In a bivariate case (dimension equals 2), the Fréchet–Hoeffding bounds are

max{u+v-1,0} ≤ C(u,v) ≤ min{u,v}

The upper bound corresponds to the case of comonotonicity (perfect positive dependence) and the lower bound corresponds to the case of countermonotonicity (perfect negative dependence).

Sklar’s theorem

Sklar’s theorem states that every multivariate cumulative distribution function of a random vector X can be expressed in terms of its marginals and a copula. The copula is unique if the marginal distributions are continuous. The theorem states also that the converse is true.

Sklar’s theorem shows how a unique copula C fully describes the dependence of X. The theorem provides a way to decompose a multivariate joint distribution function into its marginal distributions and a copula function.

Examples of copulas

Many types of dependence structures exist, and new copulas are being introduced by researchers. There are three standard classes of copulas that are commonly in use among practitioners: elliptical or normal copulas, Archimedean copulas, and extreme value copulas.

Elliptical or normal copulas

The Gaussian copula and the Student-t copula are among this category. Be reminded that the Gaussian copula played a notable role in the 2008 financial crisis, particularly in the context of mortgage-backed securities and collateralized debt obligations (CDOs). The assumption of normality and underestimation of systemic risk based on the Gaussian copula failed to account for the extreme risks in face of crisis.

Here is an example of a simulated normal copula with the parameter being 0.8.

Figure 1. Simulation of normal copula.

Source: computation by the author.

Archimedean copulas

Archimedean copulas are a class of copulas that have a particular mathematical structure based on Archimedean copula families. These copulas have a connection with certain mathematical functions known as Archimedean generators.

Here is an example of a simulated Clayton copula with the parameter being 3, which is from the category of Archimedean copulas

Figure 2. Simulation of Clayton copula.

Source: computation by the author.

Extreme value copulas

Extreme value copulas could overlap with the two other classes. They are a specialized class of copulas designed to model the tail dependence structure of multivariate extreme events. These copulas are particularly useful in situations where the focus is on capturing dependencies in the extreme upper or lower tails of the distribution.

Here is an example of a simulated Tawn copula with the parameter being 0.8, which is from the category of extreme value copulas

Figure 3. Simulation of Tawn copula.
Simulation of Clayton copula
Source: computation by the author.

Download R file to simulate copulas

You can find below an R file (file with txt format) to simulate the 3 copulas mentioned above.

Why should I be interested in this post?

Copulas are pivotal in risk management, offering a sophisticated approach to model the dependence among various risk factors. They play a crucial role in portfolio risk assessment, providing insights into how different assets behave together and enhancing the robustness of risk measures, especially in capturing tail dependencies. Copulas are also valuable in credit risk management, aiding in the assessment of joint default probabilities and contributing to an understanding of credit risks associated with diverse financial instruments. Their applications extend to insurance, operational risk management, and stress testing scenarios, providing a toolset for comprehensive risk evaluation and informed decision-making in dynamic financial environments.

Useful resources

Course notes from Quantitative Risk Management of Prof. Marie Kratz, ESSEC Business School.

About the author

The article was written in November 2023 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).

Bitcoin: the mother of all cryptocurrencies

October 26, 2023 by Snehasish CHINARA

Bitcoin: the mother of all cryptocurrencies

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains Bitcoin which is considered as the mother of all cryptocurrencies.

Historical context and background

The genesis of Bitcoin can be traced back to the aftermath of the Financial Crisis of 2008, when a growing desire emerged for a currency immune to central authority control. Traditional banks had faltered, leading to the devaluation of money through government-sanctioned printing. The absence of a definitive limit on money creation fostered uncertainty. Bitcoin ingeniously addressed this quandary by establishing a fixed supply of coins and a controlled production rate through transparent coding. This code’s openness ensured that no entity, including governments, could manipulate the currency’s value. Consequently, Bitcoin’s worth became solely determined by market dynamics, evading the arbitrary alterations typical of government-managed currencies.

Furthermore, Bitcoin revolutionized financial transactions by eliminating reliance on third-party intermediaries, exemplified by banks. Users can now engage in direct peer-to-peer transactions, circumventing the potential for intermediaries to engage in risky financial ventures akin to the 2008 Financial Crisis. The process of safeguarding one’s Bitcoins is equally innovative, as users manage their funds through a Bitcoin Wallet. Unlike traditional banks, these wallets operate as personal assets, with users as their own bankers. While various companies offer wallet services, the underlying code remains accessible for review, ensuring customers’ trust and the safety of their deposits.

Bitcoin Logo

Source: internet.

Figure 1. Key Dates in Bitcoin History

Source: author of this post.

Key features and use cases

Examples of areas where Bitcoin is currently being used:

Digital Currency: Bitcoin serves as a digital currency for everyday transactions, allowing users to buy goods and services online and in physical stores.
Crypto Banking: Bitcoin is used in decentralized finance (DeFi) applications, where users can lend, borrow, and earn interest on their Bitcoin holdings.
Asset Tokenization: Bitcoin is used to tokenize real-world assets like real estate and art, making them more accessible and divisible among investors.
Onchain Governance: Some blockchain projects utilize Bitcoin for on-chain governance, enabling token holders to vote on protocol upgrades and changes.
Smart Contracts: While Ethereum is more widely associated with smart contracts, Bitcoin’s second layer solutions like RSK (Rootstock) allow for the execution of smart contracts on the Bitcoin blockchain.
Corporate Treasuries: Large corporations, such as Tesla, have invested in Bitcoin as a store of value and an asset to diversify their corporate treasuries.
State Treasuries: Some countries, like El Salvador, have adopted Bitcoin as legal tender and added it to their national treasuries to facilitate cross-border remittances and financial inclusion.
Store of Value During Times of Conflict: In regions with economic instability or conflict, Bitcoin is used as a hedge against currency devaluation and asset confiscation.
Online Gambling: Bitcoin is widely accepted in online gambling platforms, providing users with a secure and pseudonymous way to wager on games and sports.
Salary Payments for Freelancers in Emerging Markets: Freelancers in countries with limited access to traditional banking use Bitcoin to receive payments from international clients, circumventing costly and slow remittance services.
Cross-Border Transactions with Bitcoin Gold: Cross-border transactions can often be complex, time-consuming, and costly due to the involvement of multiple intermediaries and the varying regulations of different countries. However, Bitcoin Gold offers a streamlined solution for facilitating global payments, making cross-border transactions more efficient and accessible.

These examples highlight the diverse utility of Bitcoin, ranging from everyday transactions to more complex financial applications and as a tool for economic empowerment in various contexts.

Technology and underlying blockchain

Blockchain technology is the foundational innovation that underpins Bitcoin, the world’s first and most well-known cryptocurrency. At its core, blockchain is a decentralized and distributed ledger system that records transactions across a network of computers in a secure and transparent manner. In the context of Bitcoin, this blockchain serves as a public ledger that tracks every transaction ever made with the cryptocurrency. What sets blockchain apart is its ability to ensure trust and security without the need for a central authority, such as a bank or government. Each block in the chain contains a set of transactions, and these blocks are linked together in a chronological and immutable fashion. This means that once a transaction is recorded on the blockchain, it cannot be altered or deleted. This transparency, immutability, and decentralization make blockchain technology a revolutionary tool not only for digital currencies like Bitcoin but also for a wide range of applications in various industries, from finance and supply chain management to healthcare and beyond.

Moreover, Bitcoin operates on a decentralized network of computers (nodes) worldwide. These nodes validate and confirm transactions, ensuring that the network remains secure, censorship-resistant, and immune to central control. The absence of a central authority is a fundamental characteristic of Bitcoin and a key differentiator from traditional financial systems. Bitcoin relies on a PoW consensus mechanism for securing its network. Miners compete to solve complex mathematical puzzles, and the first one to solve it gets the right to add a new block of transactions to the blockchain. This process ensures the security of the network, prevents double-spending, and maintains the integrity of the ledger. Bitcoin has a fixed supply of 21 million coins, a feature hard-coded into its protocol. The rate at which new Bitcoins are created is reduced by half approximately every four years through a process known as a “halving.” This limited supply is in stark contrast to fiat currencies, which can be printed without restriction.

These technological aspects collectively make Bitcoin a groundbreaking innovation that has disrupted traditional finance and is increasingly studied and integrated into the field of finance. It offers unique opportunities and challenges for finance students to explore, including its impact on monetary policy, investment, and the broader financial ecosystem.

Supply of coins

Looking at the supply side of bitcoins, the number of bitcoins in circulation is given by the following mathematical formula:

Formula for the number of bitcoins in circulation

This calculation hinges upon the fundamental concept of the Bitcoin supply schedule, which employs a diminishing issuance rate through a process known as “halving”.

Figure 2 represents the evolution of the number of bitcoins in circulation overt time based on the above formula.

Figure 2. Number of bitcoins in circulation

Source: computation by the author.

You can download below the Excel file for the data and the figure of the number of bitcoins in circulation.

Historical data for Bitcoin

How to get the data?

The Bitcoin is the most popular cryptocurrency on the market, and historical data for the Bitcoin such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Bitcoin on Yahoo! Finance (the Yahoo! code for Bitcoin is BTC-USD).

Figure 4. Bitcoin data

Source: Yahoo! Finance.

Historical data for the Bitcoin market prices

The market price of Bitcoin is a dynamic and intricate element that reflects a multitude of factors, both intrinsic and extrinsic. The gradual rise in market value over time indicates a willingness among investors and traders to offer higher prices for the cryptocurrency. This signifies a rising interest and strong belief in the project’s potential for the future. The market price reflects the collective sentiment of investors and traders. Comparing the market price of Bitcoin to other similar cryptocurrencies or benchmark assets can provide insights into its relative strength and performance within the market.

The value of Bitcoin in the market is influenced by a variety of elements, with each factor contributing uniquely to their pricing. One of the most significant influences is market sentiment and investor psychology. These factors can cause prices to shift based on positive news, regulatory changes, or reactive selling due to fear. Furthermore, the real-world implementations and usages of Bitcoin are crucial for its prosperity. Concrete use cases such as Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and international transactions play a vital role in creating demand and propelling price appreciation. Meanwhile, adherence to basic economic principles is evident in the supply-demand dynamics, where scarcity due to limited issuance, halving events, and token burns interact with the balance between supply and demand.

With the number of coins in circulation, the information on the price of coins for a given currency is also important to compute Bitcoin’s market capitalization.

Figure 5 below represents the evolution of the price of Bitcoin in US dollar over the period October 2014 – August 2023. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 5. Evolution of the Bitcoin price

Source: computation by the author (data source: Yahoo! Finance).

Python code

Python script to download Bitcoin historical data and save it to an Excel sheet::

import yfinance as yf
import pandas as pd

# Define the ticker symbol and date range
ticker_symbol = “BTC-USD”
start_date = “2020-01-01”
end_date = “2023-01-01”

# Download historical data using yfinance
data = yf.download(ticker_symbol, start=start_date, end=end_date)

# Create a Pandas DataFrame
df = pd.DataFrame(data)

# Create a Pandas Excel writer object
excel_writer = pd.ExcelWriter(‘bitcoin_historical_data.xlsx’, engine=’openpyxl’)

# Write the DataFrame to an Excel sheet
df.to_excel(excel_writer, sheet_name=’Bitcoin Historical Data’)

# Save the Excel file
excel_writer.save()

print(“Data has been saved to bitcoin_historical_data.xlsx”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

The code above allows you to download the data from Yahoo! Finance.

R code

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Bitcoin.

Data file

The R program that you can download above allows you to download the data for the Bitcoin from the Yahoo! Finance website. The database starts on September 17, 2014.

Table 3 below represents the top of the data file for the Bitcoin downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the Bitcoin.
Top of the file for the Bitcoin data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the Bitcoin

Figure 6 below gives the evolution of the Bitcoin from September 17, 2014 to December 31, 2022 on a daily basis.

Figure 6. Evolution of the Bitcoin.

Source: computation by the author (data: Yahoo! Finance website).

Figure 7 below gives the evolution of the Bitcoin returns from September 17, 2014 to December 31, 2022 on a daily basis.

Figure 7. Evolution of the Bitcoin returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Bitcoin

The R program that you can download above also allows you to compute summary statistics about the returns of the Bitcoin.

Table 4 below presents the following summary statistics estimated for the Bitcoin:

The mean
The standard deviation (the squared root of the variance)
The skewness
The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the Bitcoin.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Bitcoin returns

Historical distribution

Figure 8 represents the historical distribution of the Bitcoin daily returns for the period from September 17, 2014 to December 31, 2022.

Figure 8. Historical distribution of the Bitcoin returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from September 17, 2014 to December 31, 2022. The annualized mean of daily returns is equal to 30.81% and the annualized standard deviation of daily returns is equal to 62.33%.

Figure 9 below represents the Gaussian distribution of the Bitcoin daily returns with parameters estimated over the period from September 17, 2014 to December 31, 2022.

Figure 9. Gaussian distribution of the Bitcoin returns.
Gaussian distribution of the daily Bitcoin returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Bitcoin returns

The R program that you can download above also allows you to compute risk measures about the returns of the Bitcoin.

Table 5 below presents the following risk measures estimated for the Bitcoin:

The long-term volatility (the unconditional standard deviation estimated over the entire period)
The short-term volatility (the standard deviation estimated over the last three months)
The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the Bitcoin.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the Bitcoin while the study of the right tail is relevant for an investor holding a short position in the Bitcoin.

Why should I be interested in this post?

Students would be keenly interested in this article discussing Bitcoin’s history and trends due to its profound influence on the financial landscape. Bitcoin, as a novel and dynamic asset class, presents a unique opportunity for students to explore the evolving world of finance. By delving into Bitcoin’s past, understanding its market trends, and assessing its impact on global economies, students can equip themselves with the knowledge and skills needed to navigate a financial landscape that is increasingly intertwined with cryptocurrencies and blockchain technology. Moreover, this knowledge can enhance their career prospects in an industry undergoing significant transformation and innovation.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance

Yahoo! Finance Historical data for Bitcoin

CoinMarketCap Historical data for Bitcoin

About the author

The article was written in September 2023 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Extreme returns and tail modelling of the S&P 500 index for the US equity market

October 20, 2023 by Shengyu ZHENG

Extreme returns and tail modelling of the S&P 500 index for the US equity market

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) describes the statistical behavior of extreme returns of the S&P 500 index for the US equity market and explains how extreme value theory can be used to model the tails of its distribution.

The S&P 500 index for the US equity market

The S&P 500, or the Standard & Poor’s 500, is a renowned stock market index encompassing 500 of the largest publicly traded companies in the United States. These companies are selected based on factors like market capitalization and sector representation, making the index a diversified and reliable reflection of the U.S. stock market. It is a market capitalization-weighted index, where companies with larger market capitalization represent a greater influence on their performance. The S&P 500 is widely used as a benchmark to assess the health and trends of the U.S. economy and as a performance reference for individual stocks and investment products, including exchange-traded funds (ETF) and index funds. Its historical significance, economic indicator status, and global impact contribute to its status as a critical barometer of market conditions and overall economic health.

Characterized by its diversification and broad sector representation, the S&P 500 remains an essential tool for investors, policymakers, and economists to analyze market dynamics. This index’s performance, affected by economic data, geopolitical events, corporate earnings, and market sentiment, can provide valuable insights into the state of the U.S. stock market and the broader economy. Its rebalancing ensures that it remains current and representative of the ever-evolving landscape of American corporations. Overall, the S&P 500 plays a central role in shaping investment decisions and assessing the performance of the U.S. economy.

In this article, we focus on the S&P 500 index of the timeframe from April 1st, 2015, to April 1st, 2023. Here we have a line chart depicting the evolution of the index level of this period. We can observe the overall increase with remarkable drops during the covid crisis (2020) and the Russian invasion in Ukraine (2022).

Figure 1 below gives the evolution of the S&P 500 index from April 1, 2015 to April 1, 2023 on a daily basis.

Figure 1. Evolution of the S&P 500 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the daily logarithmic returns of S&P 500 index from April 1, 2015 to April 1, 2023 on a daily basis. We observe concentration of volatility reflecting large price fluctuations in both directions (up and down movements). This alternation of periods of low and high volatility is well modeled by ARCH models.

Figure 2. Evolution of the S&P 500 index logarithmic returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the S&P 500 index

Table 1 below presents the summary statistics estimated for the S&P 500 index:

Table 1. Summary statistics for the S&P 500 index.
summary statistics of the S&P 500 index returns
Source: computation by the author (data: Yahoo! Finance website).

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively. We can conclude that during this timeframe, the S&P 500 index takes on a slight upward trend, with relatively important daily deviation, negative skewness and excess of kurtosis.

Tables 2 and 3 below present the top 10 negative daily returns and top 10 positive daily returns for the S&P 500 index over the period from April 1, 2015 to April 1, 2023.

Table 2. Top 10 negative daily returns for the S&P 500 index.
Top 10 negative returns of the S&P 500 index
Source: computation by the author (data: Yahoo! Finance website).

Table 3. Top 10 positive daily returns for the S&P 500 index.
Top 10 positive returns of the S&P 500 index
Source: computation by the author (data: Yahoo! Finance website).

Modelling of the tails

The POT approach takes into account all data entries above a designated high threshold u. The threshold exceedances could be fitted into a generalized Pareto distribution:

Illustration of the POT approach

Table 4. Estimate of the parameters of the GPD for negative daily returns for the S&P 500 index.

Source: computation by the author (data: Yahoo! Finance website).

Table 5. Estimate of the parameters of the GPD for positive daily returns for the S&P 500 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 3. GPD for the left tail of the S&P 500 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Figure 4. GPD for the right tail of the S&P 500 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Applications in risk management

Why should I be interested in this post?

Download R file to model extreme behavior of the index

You can find below an R file (file with txt format) to study extreme returns and model the distribution tails for the S&P 500 index.

Useful resources

Academic resources

Embrechts P., C. Klüppelberg and T. Mikosch (1997) Modelling Extremal Events for Insurance and Finance Springer-Verlag.

Embrechts P., R. Frey, McNeil A.J. (2022) Quantitative Risk Management Princeton University Press.

Gumbel, E. J. (1958) Statistics of extremes New York: Columbia University Press.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Other resources

Extreme Events in Finance

Chan S. Statistical tools for extreme value analysis

Rieder H. E. (2014) Extreme Value Theory: A primer (slides).

About the author

The article was written in October 2023 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).

Les distributions statistiques

October 16, 2023 by Shengyu ZHENG

Distributions statistiques : variable discrète vs variable continue

Dans cet article, Shengyu ZHENG (ESSEC Business School, Grande Ecole – Master in Management, 2020-2024) explique les distributions statistiques pour des variables aléatoires discrètes et continues.

Variables aléatoires discrète et continue

Une variable aléatoire est une variable dont la valeur est déterminée d’après la réalisation d’un événement aléatoire. Plus précisément, la variable (X) est une fonction mesurable depuis un ensemble de résultats (Ω) à un espace mesurable (E).

X : Ω → E

On distingue principalement deux types de variables aléatoires : discrètes et continues.

Une variable aléatoire discrète prend des valeurs dans un ensemble dénombrable comme l’ensemble des entiers naturels. Par exemple, le nombre de points marqués lors d’un match de basket est une variable aléatoire discrète, car elle ne peut prendre que des valeurs entières telles que 0, 1, 2, 3, etc. Les probabilités associées à chaque valeur possible de la variable aléatoire discrète sont appelées probabilités de masse.

En revanche, une variable aléatoire continue prend des valeurs dans un ensemble non dénombrable comme l’ensemble des nombres réels. Par exemple, la taille ou le poids d’une personne sont des variables aléatoires continues, car elles peuvent prendre n’importe quelle valeur réelle positive. Les probabilités associées à une variable aléatoire continue sont déterminées par une fonction de densité de probabilité. Cette fonction permet de mesurer la probabilité que la variable aléatoire se situe dans un intervalle donné de valeurs.

Méthodes pour décrire des distributions statistiques

Afin de mieux comprendre une variable aléatoire, il y a plusieurs moyens pour décrire la distribution de la variable.

Calcul des statistiques

Une statistique est le résultat d’une suite d’opérations appliquées à un ensemble d’observations appelé échantillon et une mesure numérique qui résume une caractéristique de cet ensemble. Par exemple, la moyenne est un exemple de statistiques.
Les statistiques peuvent être divisées en deux types principaux : les statistiques descriptives et les statistiques inférentielles.

Les statistiques descriptives sont utilisées pour résumer et décrire les caractéristiques de base d’un ensemble de données. Elles comprennent des mesures telles que les moments d’une distribution (la moyenne, la variance, le skewness, le kurtosis, …). Une explication plus détaillée est disponible dans l’article Moments de la distribution.

Les statistiques inférentielles, quant à elles, sont utilisées pour faire des inférences sur une population à partir d’un échantillon de données. Elles incluent des tests d’hypothèses, des intervalles de confiance, des analyses de régression, des modèles prédictifs, etc.

Histogramme

Un histogramme est un type de graphique qui permet de représenter la distribution des données d’un échantillon. Il est constitué d’une série de rectangles verticaux, où chaque rectangle représente une plage de valeurs de la variable étudiée (appelée classe), et dont la hauteur correspond à la fréquence des observations de cette classe.

L’histogramme est un outil très utilisé pour visualiser la distribution des données et pour identifier les tendances et les formes dans les données pour les variables discrètes ainsi que continues discrétisées.

Fonction de masse et fonction de densité

Une fonction de masse de probabilité est une fonction mathématique qui permet de décrire la distribution de probabilité d’une variable aléatoire discrète.

La fonction de masse de probabilité associe à chaque valeur possible de la variable aléatoire discrète une probabilité. Par exemple, si X est une variable aléatoire discrète prenant les valeurs 1, 2, 3 et 4 avec des probabilités respectives de 0,2, 0,3, 0,4 et 0,1, alors la fonction de masse de probabilité de X (loi multinomiale) est donnée par :
P(X=1) = 0,2
P(X=2) = 0,3
P(X=3) = 0,4
P(X=4) = 0,1

Il est important de noter que la somme des probabilités pour toutes les valeurs possibles de la variable aléatoire doit être égale à 1, c’est-à-dire, pour toute variable aléatoire discrète X :
∑ P(X=x) = 1

Figure 1. Fonction de masse d’une loi multinomiale (pour une variable discrète).

Source : calcul par l’auteur

Par contre, une fonction de densité représente la distribution de probabilité d’une variable aléatoire continue. La fonction de densité permet de calculer la probabilité que la variable aléatoire prenne une valeur dans un intervalle donné.
Graphiquement, l’aire sous la courbe de la fonction de densité entre deux valeurs a et b correspond à la probabilité que la variable aléatoire prenne une valeur dans l’intervalle [a, b].

Il est important de noter que la fonction de densité est une fonction continue, positive et intégrable sur tout son domaine. L’intégrale de la fonction de densité sur l’ensemble des valeurs possibles de la variable aléatoire est égale à 1.

Figure 2. Fonction de densité d’une loi normale (pour une variable continue).

Source : calcul par l’auteur

Fonction de répartition

La fonction de répartition (ou fonction de distribution cumulative) est une fonction mathématique qui décrit la probabilité qu’une variable aléatoire prenne une valeur inférieure ou égale à une certaine valeur donnée. Elle est définie pour toutes les variables aléatoires, qu’elles soient continues ou discrètes.
Pour une variable aléatoire discrète, la fonction de répartition F(x) est définie comme la somme des probabilités des valeurs inférieures ou égales à x :

F(x) = P(X ≤ x) = Σ P(X = xi) pour xi ≤ x

Pour une variable aléatoire continue, la fonction de répartition F(x) est définie comme l’intégrale de la densité de probabilité f(x) de -∞ à x :
F(x)=P(X≤x)= ∫_-∞^xf(t)dt

Exemples

Dans cette partie, nous allons prendre deux exemples d’analyse de distribution statistique, l’un d’une variable aléatoire discrète et l’autre d’une variable continue.

Variable discrète : résultat du lancer d’un dé à six faces

Le jeu de lancer de dé à six faces consiste à lancer un dé pour obtenir un résultat aléatoire entre 1 et 6, correspondant aux six faces du dé. Les résultats ne prennent que les valeurs entières (1, 2, 3, 4, 5 et 6) et ils ont tous une probabilité identique de 1/6.

Dans cet exemple, le code R permet de simuler N lancers de dé et de visualiser la distribution des N résultats à l’aide d’un histogramme. En utilisant ce code, il est possible de simuler des parties de lancer de dé et d’analyser les résultats pour mieux comprendre la distribution des probabilités.

Si cette expérience aléatoire est répétée 1 000 fois, nous arrivons à un résultat dont l’histogramme est comme :

Figure 3. Histogramme des résultats de lancers d’un dé à six faces.

Source : calcul par l’auteur

Nous constatons que les résultats sont distribués d’une manière équilibrée et ont la tendance de converger vers la probabilité théorique 1/6.

Variable continue : rendments de l’indice CAC40

Le rendement d’un indice d’actions comme le CAC 40 pour le marché français est une variable aléatoire continue parce qu’elle peut prendre toutes les valeurs réelles.

Nous utilisons un historique de l’indice boursier journalier pour des cours de clôture de l’indice CAC 40 du 1^er avril 2021 au 1^er avril 2023 pour calculer des rendements journalières (rendements logarithmiques).

En finance, la distribution des rendements journalières de l’indice CAC 40 est souvent modélisée par une loi normale, même si la loi normale ne modélise pas forcément bien la distribution observée, surtout les queues de distributions observées. Dans le graphique ci-dessous, nous voyons que la distribution normale ne décrit pas bien la distribution réelle.

Figure 4. Fonction de densité des rendements journalières de l’indice CAC 40 (variable continue).

Source : calcul par l’auteur

Pour des observations issues pour une variable continue, il est toujours possible de regrouper les observations dans des intervalles et de représenter dans un histogramme.

La table 1 ci-dessous donne les statistiques descriptives pour les rendements journalières de l’indice CAC 40.

Table 1. Statistiques descriptives pour les rendements journalières de l’indice CAC 40.

Statistiques descriptives	Valeur
Moyenne	0.035
Médiane	0.116
Écart-type	1.200
Skewness	-0.137
Kurtosis	6.557

Les résultats du calcul des statistiques descriptives correspondent bien à ce que nous pouvons remarquer du graphique. La distribution des rendements a une moyenne légèrement positive. La queue de la distribution empirique est plus épaisse que celle de la distribution normale vu les survenances des rendements (positives ou négatives) extrêmes.

Fichier R pour cet article

A propos de l’auteur

Cet article a été écrit en octobre 2023 par Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).

Application de la théorie des valeurs extrêmes en finance de marchés

February 4, 2024October 6, 2023 by Gabriel FILJA

Dans cet article, Gabriel FILJA (ESSEC Business School, Executive Master in Senior Bank Management, 2022-2023 & Head of Hedging à Convera) présente des applications de la théorie des valeurs extrêmes en finance de marchés et notamment en gestion des risques de marchés.

Principe

La théorie des valeurs extrêmes (TVE), appelé théorème de Fisher-Tippet-Gnedenko tente de fournir une caractérisation complète du comportement de la queue pour tous les types de distributions de probabilités.

La théorie des valeurs extrêmes montre que la loi asymptotique des rentabilités minimale et maximale a une forme bien déterminée qui est largement indépendante du processus de rentabilités lui-même (le lien entre les deux distributions apparaît en particulier dans la valeur de l’indice de queue qui reflète le poids des queues de distribution). L’intérêt de la TVE dans la gestion du risque c’est de pouvoir calculer le quantile au-delà de 99% du seuil de confiance dans le cadre des stress tests ou de la publication des exigences réglementaires.

Gnedenko a démontré en 1943 par la Théorie des valeurs extrêmes la propriété qui s’applique à des nombreuses distributions de probabilités. Soit F(x) la fonction de répartition d’une variable x. u est une valeur de x située dans la partie droite de la queue de distribution.

La probabilité que x soit compris entre u et u+y est de F(y+u) – F(u) et la probabilité que x soit supérieur à u est 1-F(u). Soit F_u(y) la probabilité conditionnelle que x soit compris entre u et u+y sachant que x>u∶

Probabilité conditionnelle

Estimation des paramètres

Selon les résultats de Gnedenko, pour un grand nombre de distribution, cela converge vers une distribution généralisée de Pareto au fur et à mesure que u augmente :

Distribution_généralisée_Pareto

β est le paramètre d’échelle représente la dispersion de la loi des extrêmes
ξ est l’indice de queue qui mesure l’épaisseur de la queue et la forme

Selon la valeur de l’indice de queue, on distingue trois formes dedistribiution d’extrêmes :

Frechet ξ > 0
Weibull ξ < 0
Gumbel ξ = 0

L’indice de queue ξ reflète le poids des extrêmes dans la distribution des rentabilités. Une valeur positive de l’indice de queue signifie que les extrêmes n’ont pas de rôle important puisque la variable est bornée. Une valeur nulle donne relativement peu d’extrêmes alors qu’une valeur négative implique un grand nombre d’extrêmes (c’est le cas de la loi normale).

Figure 1 : Densité des lois des valeurs extrêmes

Source : auteur.

Tableau 1 : Fonctions de distribution des valeurs extrêmes pour un ξ > 0, loi de Frechet, ξ < 0 loi de Weibull et ξ = 0, loi de Gumbel.
Source : auteur.

Les paramètres β et ξ sont estimés par la méthode de maximum de vraisemblance. D’abord il faut définir u (valeur proche du 95e centile par exemple). Une des méthodes pour déterminer ce seuil, c’est la technique appelée Peak Over Threshold (POT), ou méthode des excès au-delà d’un seuil qui se focalise sur les observations qui dépassent un certain seuil donné. Au lieu de considérer les valeurs maximales ou les plus grandes valeurs, cette méthode consiste à examiner toutes les observations qui franchissent un seuil élevé préalablement fixé.
L’objectif est de sélectionner un seuil adéquat et d’analyser les excès qui en découlent. Ensuite nous trions les résultats par ordre décroissant pour obtenir les observations telles que x>u et leur nombre total.

Nous étudions maintenant les rentabilités extrêmes pour l’action Société Générale sur la période 2011-2021. La Figure 2 représentes rentabilités journalières de l’action et les rentabilités extrêmes négatives obtenues avec l’approche des dépassements de seuil (Peak Over Threshold ou POT). Avec le seuil retenu de -7%, on obtient 33 dépassements sur 2 595 rentabilités journalières de la période 2011 à 2021.

Figure 2 : Sélection des rentabilités extrêmes négatives pour l’action Société Générale selon l’approche Peak Over Threshold (POT)

Source : auteur.

Méthode d’estimation statistique

Nous allons maintenant voir comment déterminer les β et ξ en utilisant la fonction de maximum de vraisemblance qui s’écrit :

Fonction de vraisemblance

Pour un échantillon de n observations, l’estimation de 1-F(u) est n_u/n. Dans ce cas, la probabilité inconditionnelle de x>u+y vaut :

Fonction de vraisemblance

Et l’estimateur de la queue de distribution de probabilité cumulée de x (pour un grand) est :

Estimateur queue distribution

Mon travail personnel a consisté à estimer le paramètre d’échelle β et le paramètre de queue ξ à partir de la formule par le maximum de vraisemblance en utilisant le solveur Excel. Nous avons précédemment déterminé n=0,07 par la méthode de POT en Figure 2, et n_u= 2595

Ainsi nous obtenons β=0,0378 et ξ=0,0393 ce qui maximise par la méthode du maximum de vraisemblance la somme du logarithme des valeurs extrêmes à un total de 73,77.

Estimation de la VaR TVE

Pour calculer le VaR au seuil q, nous obtenons F(VaR) = q

VaR TVE

Mon travail personnel a consisté à estimer la VaR du titre de la Société Générale de la période de 2011 à 2021 sur un total de 2595 cotations avec 33 dépassements de seuil (-7%). En appliquant les données obtenues à la formule nous obtenons :

VaR 99% Société Générale

Puis nous estimons la VaR à 99,90% et 99,95% :

VaR 99,90% Société Générale

Il n’est pas surprenant que l’extrapolation à la queue d’une distribution de probabilité soit difficile, pas parce qu’il est difficile d’identifier des distributions de probabilité possibles qui pourraient correspondre aux données observées (il est relativement facile de trouver de nombreuses distributions possibles différentes), mais parce que l’éventail des réponses qui peuvent vraisemblablement être obtenues peut être très large, en particulier si nous voulons extrapoler dans la queue lointaine où il peut y avoir peu ou pas de points d’observation directement applicables.

La théorie des valeurs extrêmes, si elle est utilisée pour modéliser le comportement de la queue au-delà de la portée de l’ensemble de données observées, est une forme d’extrapolation. Une partie de la cause du comportement à queue épaisse (fat tail) est l’impact que le comportement humain (y compris le sentiment des investisseurs) a sur le comportement du marché.

En quoi ça peut m’intéresser ?

Nous pouvons ainsi mener des stress tests en utilisant la théorie des valeurs extrêmes et évaluer les impacts sur le bilan de la banque ou encore déterminer les limites de risques pour le trading et obtenir ainsi une meilleure estimation du worst case scenario.

Autres articles sur le blog SimTrade

▶ Shengyu ZHENG Catégories de mesures de risques

▶ Shengyu ZHENG Moments de la distribution

▶ Shengyu ZHENG Extreme Value Theory: the Block-Maxima approach and the Peak-Over-Threshold approach

Ressources

Articles académiques

Falk M., J. Hüsler, et R.-D. Reiss, Laws of Small Numbers: Extremes and Rare Events. Basel: Springer Basel, 2011. doi: 10.1007/978-3-0348-0009-9.

Gilli M. et E. Këllezi, « An Application of Extreme Value Theory for Measuring Financial Risk », Comput Econ, vol. 27, no 2, p. 207‑228, mai 2006, doi: 10.1007/s10614-006-9025-7.

Gkillas K. and F. Longin (2018) Financial market activity under capital controls: lessons from extreme events Economics Letters, 171, 10-13.

Gnedenko B., « Sur La Distribution Limite Du Terme Maximum D’Une Serie Aleatoire », Annals of Mathematics, vol. 44, no 3, p. 423‑453, 1943, doi: 10.2307/1968974.

Hull J.et A. White, « Optimal delta hedging for options », Journal of Banking & Finance, vol. 82, p. 180‑190, sept. 2017, doi: 10.1016/j.jbankfin.2017.05.006.

Longin F. (1996) The asymptotic distribution of extreme stock market returns Journal of Business, 63, 383-408.

Longin F. (2000) From VaR to stress testing : the extreme value approach Journal of Banking and Finance, 24, 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Longin F. and B. Solnik (2001) Extreme Correlation of International Equity Markets, The Journal of Finance, 56, 649-676.

Roncalli T. et G. Riboulet, « Stress testing et théorie des valeurs extrêmes : une vision quantitée du risque extrême ».

Sites internet

Extreme Events in Finance

A propos de l’auteur

Cet article a été écrit en juillet 2023 par Gabriel FILJA (ESSEC Business School, Executive Master in Senior Bank Management, 2022-2023 & Head of Hedging à Convera).

How to get crypto data

March 25, 2024October 4, 2023 by Snehasish CHINARA

How to get crypto data

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains how to get crypto data.

Types of data

Number of coins

The information on the number of coins in circulation for a given currency is important to compute its market capitalization. Market capitalization is calculated by multiplying the current price of the cryptocurrency by its circulating number of coins (supply). This metric gives a rough estimate of the cryptocurrency’s total value within the market and its relative size compared to other cryptocurrencies. A lower circulating supply often implies a greater level of scarcity and rarity.

For cryptocurrencies (unlike fiat money), the number of coins in circulation is given by a mathematical formula. The number of coins may be limited (like the Bitcoin) or unlimited (like Ethereum and Dogecoin) over time.

Cryptocurrencies with limited supplies, such as Bitcoin’s maximum supply of 21 million coins, can be perceived as more valuable due to their finite nature. Scarcity can contribute to investor interest and potential price appreciation over time. A lower circulating supply might indicate the potential for future adoption and value appreciation, as the limited supply can create scarcity-driven demand, especially if the cryptocurrency gains more utility and usage.

Bitcoin’s blockchain also relies on a key equation to steadily allow new BTC to be introduced. The equation below gives the total supply of bitcoins:

Total supply of bitcoins

Figure 1 below represents the evolution of the supply of Bitcoins.

Figure 1. Evolution of the supply of Bitcoins

Source: computation by the author.

Market price of a coin

The market price of a cryptocurrency in the market holds crucial insights into how well the cryptocurrency is faring. Although not the sole factor, the market price significantly contributes to evaluating the cryptocurrency’s performance and its prospects. The market price of a cryptocurrency is a dynamic and intricate element that reflects a multitude of factors, both intrinsic and extrinsic. The gradual rise in market value over time indicates a willingness among investors and traders to offer higher prices for the cryptocurrency. This signifies a rising interest and strong belief in the project’s potential for the future. The market price reflects the collective sentiment of investors and traders. Comparing the market price of a cryptocurrency to other similar cryptocurrencies or benchmark assets like Bitcoin can provide insights into its relative strength and performance within the market. A rising market price can indicate increasing adoption of the cryptocurrency for various use cases. Successful projects tend to attract more users and real-world applications, which can drive up the price.

The value of cryptocurrencies in the market is influenced by a variety of elements, with each factor contributing uniquely to their pricing. One of the most significant influences is market sentiment and investor psychology. These factors can cause prices to shift based on positive news, regulatory changes, or reactive selling due to fear. Furthermore, the real-world implementation and usage of a cryptocurrency are crucial for its prosperity. Concrete use cases such as Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and international transactions play a vital role in creating demand and propelling price appreciation. Meanwhile, adherence to basic economic principles is evident in the supply-demand dynamics, where scarcity due to limited issuance, halving events, and token burns interact with the balance between supply and demand.

With the number of coins in circulation, the information on the price of coins for a given currency is also important to compute its market capitalization.

Figure 2 below represents the evolution of the price of Bitcoin in US dollar over the period October 2014 – August 2023. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 2. Evolution of the Bitcoin price

Source: computation by the author (data source: Yahoo! Finance).

Trading volume

Trading volume is crucial when assessing the health, reliability, and potential price movements of a cryptocurrency. Trading volume refers to the total amount of a cryptocurrency that is bought and sold within a specific time frame, typically measured in units of the cryptocurrency (e.g., BTC) or in terms of its equivalent value in another currency (e.g., USD).

Trading volume directly mirrors market liquidity, with higher volumes indicative of more liquid markets. This liquidity safeguards against drastic price fluctuations when trading, contrasting with low-volume scenarios that can breed volatility, where even a single substantial trade may disproportionately shift prices. Price alterations are most reliable and meaningful when accompanied by substantial trading volume. Price movements upheld by heightened volume often hold greater validity, potentially pointing to more pronounced market sentiment. When price surges parallel rising trading volume, it suggests a sustainable upward trajectory. Conversely, low trading volume amid rising prices may hint at a forthcoming correction or reversal. Scrutinizing the correlation between price oscillations and trading volume can uncover potential divergences. For instance, ascending prices coupled with dwindling trading volume may suggest a weakening trend.

Figure 3 below represents the evolution of the monthly trading volume of Bitcoin over the period October 2014 – July 2023.

Figure 3. Evolution of the trading volume of Bitcoin

Source: computation by the author (data source: Yahoo! Finance).

Bitcoin data

You can download the Excel file with Bitcoin data used in this post as an illsutration.

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Bitcoin historical data and save it to an Excel sheet:

import yfinance as yf
import pandas as pd

# Define the ticker symbol and date range
ticker_symbol = “BTC-USD”
start_date = “2020-01-01”
end_date = “2023-01-01”

# Download historical data using yfinance
data = yf.download(ticker_symbol, start=start_date, end=end_date)

# Create a Pandas DataFrame
df = pd.DataFrame(data)

# Create a Pandas Excel writer object
excel_writer = pd.ExcelWriter(‘bitcoin_historical_data.xlsx’, engine=’openpyxl’)

# Write the DataFrame to an Excel sheet
df.to_excel(excel_writer, sheet_name=’Bitcoin Historical Data’)

# Save the Excel file
excel_writer.save()

print(“Data has been saved to bitcoin_historical_data.xlsx”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

APIs

Calculating the total number of Bitcoins in circulation over time
Access – Bitcoin Blockchain data
By running a Bitcoin node or by using blockchain data providers like Blockchain.info, Blockchair, or a similar service.

Extract Block Data: Once you have access to the blockchain data, you would need to extract information from each block. Each block contains a record of the transactions that have occurred, including the creation (mining) of new Bitcoins in the form of a “Coinbase” transaction.

Calculate Cumulative Supply: You can calculate the cumulative supply of Bitcoins by adding up the rewards from each block’s Coinbase transaction. Initially, the block reward was 50 Bitcoins, but it halves approximately every four years due to the Bitcoin halving events. So, you’ll need to account for these halving in your calculations.

Code – python

import requests

# Replace ‘YOUR_API_KEY’ with your CoinMarketCap API key
api_key = ‘YOUR_API_KEY’

# Define the endpoint URL for CoinMarketCap’s API
url = ‘https://pro-api.coinmarketcap.com/v1/cryptocurrency/quotes/latest’

# Define the parameters for the request
params = {
‘symbol’: ‘BTC’,
‘convert’: ‘USD’,
‘CMC_PRO_API_KEY’: api_key
}

# Send the request to CoinMarketCap
response = requests.get(url, params=params)

# Parse the response JSON
data = response.json()

# Extract the circulating supply from the response
circulating_supply = data[‘data’][‘BTC’][‘circulating_supply’]

print(f”Current circulating supply of Bitcoin: {circulating_supply} BTC”)

## Replace ‘YOUR_API_KEY’ with your actual CoinMarketCap API key.

Why should I be interested in this post?

Cryptocurrency data is becoming increasingly relevant in these fields, offering opportunities for research, data analysis skill development, and even career prospects. Whether you’re aiming to conduct research, stay informed about the evolving financial landscape, or simply enhance your data analysis abilities, understanding how to access and work with crypto data is an asset. Plus, as the cryptocurrency industry continues to grow, this knowledge can open new career paths and improve your personal finance decision-making. In a rapidly changing world, diversifying your knowledge with cryptocurrency data acquisition skills can be a wise investment in your future.

Useful resources

APIs

CoinMarketCap Source of API keys and program

CoinGecko Source of API keys and Programs

CryptoNews Source of API keys and Programs

Data sources

Yahoo! Finance Historical data for Bitcoin

Coinmarketcap Historical data for Bitcoin

Blockchain.com Market Data and charts on Bitcoin history

About the author

The article was written in October 2023 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, (2022-2024).

Market Capitalization

June 28, 2023 by Nithisha CHALLA

Market Capitalization

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) explains Market Capitalization and its specificities.

What is Market Capitalization?

Market capitalization is a key metric used to assess the size and value of publicly traded companies. It represents the company’s value for the owners of the company (the shareholders or stockholders). This metric allows companies to be classified as large-cap, mid-cap, or small-cap based on their respective market-capitalization sizes.

Large-cap companies are typically more established, with market capitalizations exceeding several billion dollars. They are more stable and frequently represent industry leaders. In the US stock market, Apple, Microsoft, and Amazon are examples of large-cap companies.

Mid-cap companies fall between large-cap and small-cap companies. They are typically businesses that have seen moderate growth and may still have room for expansion. Mid-cap companies are frequently regarded as having a good balance of growth potential and stability. For example, Etsy Inc., DocuSign Inc., Spotify Technology S.A. etc.

Small-cap companies have lower market capitalizations than large-cap and mid-cap firms. They are generally thought to have greater growth potential, but also greater risk due to their smaller size and possibly limited resources. NeoGenomics, Inc., Clean Energy Fuels Corp., Axon Enterprise Inc. etc.

Mathematical formula?

The general formula for calculating market capitalization:

Market Capitalization = Current Share Price x Number of Outstanding Shares

In this formula:
“Current Share Price” refers to the price of a single share of the company’s stock. It is the latest transaction price. As Market Capitalization is usually computed every day, the current share price corresponds to the closing price of the trading session.

“Number of Outstanding Shares” represents the total number of shares of the company’s stock that are publicly available and held by investors.

The Significance of Stock Price

When considering market capitalization, the stock price is an important factor to consider. It represents the current market price at which a company’s shares are bought and sold. Stock prices, which are influenced by factors such as supply and demand, market sentiment, and company-specific news, play a critical role in determining a company’s market capitalization.

On the short term, as the number of shares issued by the company is stable, the stock price is the main factor which influences market capitalization.

How is the Number of Shares Computed?

The total number of outstanding shares of a company’s stock is used to calculate market capitalization. The outstanding shares are those that the company has issued and are held by shareholders, which include individual investors, institutional investors, and insiders.

The number of outstanding shares can be found in the company’s financial statements, specifically the balance sheet and the notes to the financial statements.

Which Shares are Included?

The outstanding shares generally include common shares or ordinary shares, which are the most common types of shares issued by companies. Preferred shares or other types of securities that may have different rights or characteristics are typically excluded from the calculation of market capitalization.

When we compute market capitalization, we take into consideration all outstanding shares of stock, which include publicly traded shares plus restricted shares held by the top management team and the founders of the company. Note that market capitalization is different from the float which takes into consideration only the shares available for trading in the secondary market.

If a company has different classes of shares with different voting rights or other characteristics, each class of shares may have its own market capitalization calculation based on the respective share price and the number of outstanding shares for that class.

Market capitalization provides an estimate of the overall value of the publicly traded portion of a company and is commonly used as a measure to compare companies or track changes in a company’s value over time.

Why should I be interested in this post?

Understanding market capitalization allows management students to analyze the financial health and performance of companies. By considering market capitalization along with other financial indicators, students can assess the relative size and value of companies in the market. Management students need to evaluate investment opportunities and determine the attractiveness of different stocks or companies based on their market capitalization and growth potential. Large-cap companies often offer stability and lower risk, while small-cap companies tend to be riskier but may have higher growth potential. Management students need to understand the risk-return tradeoff associated with different market capitalization segments.

Useful resources

Fidelity Investments Market capitalization

Wikipedia Market capitalization

Motley Fool An Example of Market Capitalization

About the author

The article was written in June 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

Can technical analysis actually help to make better trading decisions?

June 14, 2023 by Theo SCHWERTLE

Can technical analysis actually help to make better trading decisions?

In this article, Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2023) explains how technical analysis can actually help to make better trading decisions (or not).

Market efficiency

Let’s take a look at the different levels of market efficiency and their implications for a trader.

The efficient market hypothesis (EMH) posits that market prices fully incorporate all available information. If this hypothesis is verified, it is infeasible to consistently achieve higher returns than the market on a risk-adjusted basis. According to the EMH, stocks are believed to consistently trade at their fair value on exchanges, precluding the possibility of purchasing undervalued stocks or selling overvalued ones, thus implicitly dismissing the efficiency of technical analysis (TA) and fundamental analysis. As such, the EMH suggests that outperforming the overall market through security selection or market timing is infeasible, and the only way for investors to attain higher returns is by taking on increased risk in their investments.

Definitions

The EMH has three forms: the weak form, the semi-strong form and the strong form. The weak form of the EMH asserts that historical market data (transaction prices and volumes) cannot be used to predict future price movements. The semi-strong form of the EMH asserts that publicly available information (historical market data, financial account published by firms, reports written by financial analysts, etc.) cannot be used to predict future price movements. The strong form of the EMH asserts that both public and private information cannot be used to predict future movements.

Tests of the EMH

Though the strong form of the EMH is generally rejected, scholars are less consistent with evidence for or against the weak or semi-strong form of the EMH. Focusing on technical analysis, a significant body of literature has examined the relationship between EMH and technical analysis (TA), with many scholars rejecting the weak form (Leigh et al., 2002; Eugster and Uhl, 2022). The results of the tests seem to depend on the length of the investment period, the EMH being less rejected for a longer investment period.

Technical analysis

In the world of finance, Technical Analysis serves as an essential tool for investors and traders alike. The methodology involves forecasting future price movements based on the historical data of financial instruments. This strategy pivots on two core principles: the market discounts everything, and prices move in trends (Kirkpatrick & Dahlquist, 2010).

Chartism is one of the oldest techniques in technical analysis. It rests on the identification and analysis of chart patterns and price formations, with chartists meticulously studying these patterns to anticipate future market trends (Lo, Mamaysky, & Wang, 2000). This form of analysis operates on the principle that certain patterns are recurring and that understanding these patterns can provide insights into future price movements.

Another time-tested tool is Moving Averages, a technique that seeks to smooth out price data by creating a consistently updated average price. This approach comes in several variants, with the Simple Moving Average (SMA) and the Exponential Moving Average (EMA) being the most prevalent. These techniques help to clear out the ‘noise’ from random short-term price fluctuations and allow analysts to focus on the overall trend direction.

In stark contrast to these conventional methods stands the modern, technology-driven approach of High Frequency Trading (HFT). This innovative form of trading capitalizes on the power of advanced algorithms and high-speed data processing to execute trades at astonishing speeds. Unlike traditional technical analysis, which primarily focuses on transaction prices and volumes, HFT leverages real-time data from the order-flow and the order-book, exploring minute market discrepancies that might otherwise go unnoticed (Aldridge, 2010).

All we need is short-term market inefficiencies

Hirshleifer and Shumway (2003) gave meaningful insight into the relationship between the weather and daily market index return, demonstrating that sunshine is strongly and significantly correlated with stock returns. In line with that argumentation, Edmans et al. (2007) investigate the stock market reaction to sudden changes in investor mood, using international soccer results as the primary mood variable. The results show a significant market decline after soccer losses in equity markets of the losing teams, with a loss in the World Cup elimination stage leading to a next-day abnormal stock return of −49 basis points. This effect is more substantial in small stocks and more meaningful games and is robust to methodological changes. The same loss effect could also be documented for other international tournaments.

So what does that mean? There are human biases that make humans so different from the rational being many financial theories suggest we are.

Discussion about the feasibility of technical analysis for hedge funds

Hedge funds are also using technical analysis in their decision-making process; however, the degree of utilization varies significantly. The main area where TA is used by hedge funds is to find areas of liquidity to full big positions.

Kavajecz und Odders-White (2004) explored the relationship between TA and liquidity by testing the hypotheses that support and resistance levels coincide with peaks in depth on the limit order book and that moving-average forecasts reveal information about the relative position of depth on the book. They found that technical support/resistance levels, as well as moving average indicators, are significantly related to the state of liquidity on the limit order book and concluded that it is tied to the strategic behavior of limit order traders. This provides a reliable method for practitioners to locate liquidity in the book and reduce transaction costs.

The main advantage of TA is the low cost to construct a market perspective as it requires only market data. The implementation of TA is lower than acquiring and analyzing public or private information. So, if used adequately it is in face the cheaper and more accessible investment approach compared to traditional financial analysis tools.

Sounds good! Where is the catch?

According to Timmermann and Granger (2004), using new financial prediction methods may lead to short-term gains as the information is rapidly incorporated into market prices making the market the more efficient. As these new financial prediction methods become more widely used by other market participants, their effectiveness decreases over time. This idea is supported by studies showing that many stock market anomalies diminish, vanish, or even reverse after they are documented in academic literature (publication on the Social Science Research Network (SSRN) for example).

A broad study by Yamamoto (2012) investigated the profitability of exploiting short-term market inefficiencies and concluded that one could not generate consistent positive results that outperform a buy-and-hold strategy. Yamamoto (2012) analyzes technical strategies for 207 individual stocks in the Nikkei 225 over a one-year period and use two statistical procedures to reduce data-snooping bias (the data-snooping bias refers to the tendency to make false discoveries or draw incorrect conclusions when repeatedly testing and analyzing a dataset, often due to the increased likelihood of finding seemingly significant patterns or relationships by chance). The results indicate that all 9 technical trading strategies underperform the buy-and-hold strategy, suggesting that information on past prices and demand/supply imbalances are not sufficient for superior technical trading profits.

Conclusion

Short-term market inefficiencies can be exploited to generate positive returns. However, many of the found profitability diminish after introducing real market conditions, transaction fees or adjusting the returns for the increased risk. Generally, TA offers increased benefits over fundamental analysis in the short-term but loses ground with increased time as the market returns to efficiency. The difference in information costs motivates its popularity, but even if a profitable trading strategy is found, its benefits may only be enjoyed for a short time.

Why should I be interested in this post?

Technical analysis offers a different perspective on the market that is rarely touched on by university curriculums. This alternative approach is used by individual traders as well as institutional traders like hedge funds to find good entries and exits in the market. According to a survey by Menkhoff (2010), 77% of all hedge fund managers in their sample rate TA as really important to their decision-making, attributing a value of at least 10% to it in their decision-making process. About 20% of fund managers even indicate to prefer TA over fundamental analysis. So, it seems to offer some value, despite the academic criticism in line the efficiency of the market.

Useful resources

Academic articles

Edmans, A., García, D. & Norli, Y. (2007). Sports Sentiment and Stock Returns The Journal of Finance 62(4), 1967–1998.

Eugster, P. & Uhl, M. W. (2022). Technical analysis: Novel insights on contrarian trading. European Financial Management .

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance 25(2), 383-417.

Hirshleifer, D. & Shumway, T. (2003). Good Day Sunshine: Stock Returns and the Weather The Journal of Finance 58(3), 1009–1032.

Kavajecz, K. A. & Odders-White, E. R. (2004). Technical Analysis and Liquidity Provision Review of Financial Studies 17(4), 1043–1071.

Leigh, W., Purvis, R. & Ragusa, J. M. (2002). Forecasting the NYSE composite index with technical analysis, pattern recogniser, neural network, and genetic algorithm: a case study in romantic decision support Decision Support Systems 32(4), 361–377.

Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance 55(4), 1705-1770.

Menkhoff, L. (2010). The use of technical analysis by fund managers: International evidence. Journal of Banking & Finance 34(11), 2573–2586.

Timmermann, A. & Granger, C. W. (2004). Efficient market hypothesis and forecasting International Journal of Forecasting, 20(1), 15–27.

Yamamoto, R. (2012). Intraday technical analysis of individual stocks on the Tokyo Stock Exchange Journal of Banking & Finance, 36(11), 3033–3047.

Books

Aldridge, I. (2010). High-frequency trading: a practical guide to algorithmic strategies and trading systems. John Wiley & Sons.

Kirkpatrick II, C. D., & Dahlquist, J. R. (2010). Technical Analysis: The Complete Resource for Financial Market Technicians. FT press.

Lewis, M. (2014). Flash Boys: A Wall Street Revolt. W. W. Norton & Company.

About the author

The article was written in June 2023 by Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2018-2023).

The KOSPI 50 index

June 6, 2023 by Nithisha CHALLA

The KOSPI 50 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the KOSPI 50 index representing the South Korean equity market and details its characteristics.

The KOSPI 50 index

A well-known stock market index in South Korea, the KOSPI 50 index serves as a crucial benchmark for the South Korean equity market. It represents the performance of the 50 biggest and busiest companies traded on the main South Korean stock exchange, the Korea Exchange (KRX), listed on the market.

The KOSPI 50 index, which was created on April 1, 2002, is managed by the Korea Exchange and is widely regarded as an accurate indicator of the Korean economy and its key sectors. Market capitalization, trading volume, and liquidity are used in the index selection process to make sure that only the most significant and representative companies from the Korean market are included.

The KOSPI 50, a market capitalization-weighted index, takes into account the market value of each constituent stock to reflect the relative importance of each stock. The KOSPI 50 is prominently displayed on trading platforms and financial websites, similar to other significant stock market indices, making it simple for investors and analysts worldwide to access. It is a crucial indicator of the state and trends of the Korean economy and is important for making investment decisions.

The ticker symbol commonly used in the financial industry to represent the KOSPI 50 index is “KOSPI50”.

Table 1 below gives the Top 10 stocks in the KOSPI 50 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the KOSPI 50 index.

Source: computation by the author (data: Yahoo! Finance website).

Table 2 below gives the sector representation of the KOSPI 50 index in terms of number of stocks and market capitalization as of January 31, 2023.

Table 2. Sector representation in the KOSPI 50 index.

Source: computation by the author (data: Yahoo! Finance website).

Calculation of the KOSPI 50 index value

The KOSPI 50 index is a float-adjusted market-capitalization-weighted index. It is adjusted for the proportion of shares that are available for trading in the market as well as the market value of each constituent stock. With the help of this weighting methodology, investors can get a complete picture of the Korean market by ensuring that larger companies have a greater influence on the index’s movements than smaller ones.

The formula to compute the KOSPI 50 index is given by

Float Adjusted Market Capitalization Index value

where I is the index value, k a given asset, K the number of assets in the index, P^k the market price of asset k, N^k the number of issued shares for asset k, F^k the float factor of asset k, and t the time of calculation of the index.

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by

Float Adjusted Market Capitalization Weighted Index Weight

Use of the KOSPI 50 index in asset management

The analysis of the companies that make up the KOSPI 50 index offers important new perspectives on the Korean economy, its key industries, and the elements that influence business success there. The index also acts as a crucial tool for investors, allowing them to assess the performance of their portfolios in comparison to the larger Korean market and make well-informed investment choices. It supports various asset management tasks, such as passive investments, evaluating corporate risk, asset allocation, and portfolio management, and offers investors insightful information.

Benchmark for equity funds

Investors can gain a thorough understanding of the South Korean market and make wise investment decisions by following the KOSPI 50 index. It is significant to remember that the KOSPI 50 index, which includes the 50 largest and most actively traded companies in South Korea, represents a particular market segment. While it offers an accurate indicator of the performance of these well-known businesses, it might not accurately reflect the performance of all markets and industry sectors nationwide. Investors should think about incorporating other indices, such as the KOSPI 200, which covers a wider range of companies listed on the Korea Exchange, or the MSCI Korea Index, which includes a more diverse set of companies, to obtain a more thorough evaluation of the South Korean market.

Financial products around the KOSPI 50 index

Different financial products linked to the KOSPI 50 index are available for investors looking to diversify their portfolios and increase their exposure to the South Korean stock market. These products offer chances to possibly profit from changes in the market and take part in the performance of the 50 biggest and most actively traded South Korean companies.

Here are some of the main financial products associated with the KOSPI 50 index:

Exchange-Traded Funds (ETFs): similar to stocks, investors can trade and invest in ETFs that track the KOSPI 50 index. These ETFs offer a practical way to get exposure to the KOSPI 50 companies’ performance. The KODEX KOSPI 200 ETF and the Samsung KODEX Leverage ETF are two examples of KOSPI 50 ETFs.
Options and Futures Contracts: Investors can use options and futures contracts based on the KOSPI 50 index to manage risk, make predictions about market trends, or put trading strategies into practice. Investors can purchase or sell the index through these derivative contracts at predetermined future prices and dates.
Mutual Funds and Index Funds: A number of mutual funds and index funds concentrate their investments in the businesses represented by the KOSPI 50 index. These funds seek to match the performance of the index or build portfolios that closely resemble the index’s components. Through these funds, investors can gain exposure to the KOSPI 50, allowing for investment diversification and expert management.

Historical data for the KOSPI 50 index

How to get the data?

The KOSPI 50 index is the most common index used in finance, and historical data for the KOSPI 50 index can be easily downloaded from the internet.

For example, you can download data for the KOSPI 50 index from December 11, 1996 on Yahoo! Finance (the Yahoo! code for KOSPI 50 index is ^KS11).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the KOSPI 50 index.

Data file

The R program that you can download above allows you to download the data for the KOSPI 50 index from the Yahoo! Finance website. The database starts on December 11, 1996. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the KOSPI 50 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the KOSPI 50 index.
Top of the file for the KOSPI 50 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the KOSPI 50 index

Figure 1 below gives the evolution of the KOSPI 50 index from December 11, 1996 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the KOSPI 50 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the KOSPI 50 index returns from December 11, 1996 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the KOSPI 50 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the KOSPI 50 index

The R program that you can download above also allows you to compute summary statistics about the returns of the KOSPI 50 index.

Table 4 below presents the following summary statistics estimated for the KOSPI 50 index:

The mean
The standard deviation (the squared root of the variance)
The skewness
The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the KOSPI 50 index.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the KOSPI 50 index returns

Historical distribution

Figure 3 represents the historical distribution of the KOSPI 50 index daily returns for the period from December 11, 1996 to December 30, 2022.

Figure 3. Historical distribution of the KOSPI 50 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from December 11, 1996 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the KOSPI 50 index daily returns with parameters estimated over the period from December 11, 1996 to December 30, 2022.

Figure 4. Gaussian distribution of the KOSPI 50 index returns.
Gaussian distribution of the daily KOSPI 50 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the KOSPI 50 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the KOSPI 50 index.

Table 5 below presents the following risk measures estimated for the KOSPI 50 index:

The long-term volatility (the unconditional standard deviation estimated over the entire period)
The short-term volatility (the standard deviation estimated over the last three months)
The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the KOSPI 50 index.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the KOSPI 50 index while the study of the right tail is relevant for an investor holding a short position in the KOSPI 50 index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the KOSPI 50 index. The index includes wide range of industries, including energy, finance, telecommunications, and consumer goods, and it covers the biggest and most liquid German companies. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Russia or interested in investing in German equities.

Individual investors can assess the performance of their own investments in the German equity market with the KOSPI 50 index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the KOSPI 50 index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Business

Wikipedia What is the KOSPI 50 index

PWC A guide to listing on the Korean exchange

Data

Yahoo! Finance

Yahoo! Finance Historical data for the KOSPI 50 index

About the author

The article was written in June 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The OMX Copenhagen 25 (OMXC 25) index

June 6, 2023 by Nithisha CHALLA

The OMX Copenhagen 25 (OMXC 25) index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the OMX Copenhagen 25 (OMXC25 or OMXC 25) index representing the Danish equity market and details its characteristics.

The OMX Copenhagen 25 index

The 25 biggest and busiest companies listed on Nasdaq Copenhagen, the main stock exchange in Denmark, make up the OMX Copenhagen 25 (OMXC 25) index, which is a market-capitalization-weighted index. With 1,000 points as the base point, the index was introduced on December 4th, 1996.

Nasdaq Copenhagen chooses the stocks for the OMXC 25 index, taking into account elements like market capitalization, liquidity, and free float. To maintain its representation of the Danish stock market, the index is reviewed twice a year, in June and December, and rebalanced as necessary.

The OMXC 25 is a market-capitalization-weighted index, which means that the index’s weight is based on the market capitalization of each company. This increases the OMXC 25’s comparability to the Danish market as a whole.

Investors and analysts pay close attention to the performance of the OMXC 25 index, which is widely used as a benchmark for the Danish stock market. Through financial products like exchange-traded funds (ETFs) and index funds that follow the OMXC 25 index, investors can gain exposure to the Danish market. The ticker symbol “OMXC25” is frequently used in trading platforms and financial websites to denote the OMXC 25 index.

Table 1 below gives the Top 10 stocks in the OMXC 25 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the OMXC 25 index.

Source: computation by the author (data: Yahoo! Finance website).

Table 2 below gives the sector representation of the OMXC 25 index in terms of number of stocks and market capitalization as of January 31, 2023.

Table 2. Sector representation in the OMXC 25 index.

Source: computation by the author (data: Yahoo! Finance website).

Calculation of the OMXC 25 index value

The performance of the 25 most actively traded and highly capitalized companies listed on the Danish Nasdaq Copenhagen stock exchange is reflected in the OMX Copenhagen 25 (OMXC 25) index, which is a float-adjusted market-capitalization-weighted index. The index is evaluated twice a year by Nasdaq Copenhagen and includes businesses from a variety of industries, including technology, healthcare, and finance. Each year, the index is rebalanced in June and December, and the companies that make up the index are chosen using criteria like market capitalization, trading volume, and free float.

The formula to compute the OMXC 25 index is given by

Float Adjusted Market Capitalization Index value

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by

Float Adjusted Market Capitalization Weighted Index Weight

Use of the OMXC 25 index in asset management

A common benchmark used by investors to evaluate the performance of their investment portfolios in relation to the Danish stock market is the OMXC 25 index. Investors and analysts can learn a lot about the state of the Danish economy overall and the performance of important industries like technology, healthcare, and industrials by closely following the changes in the OMXC 25 index. Through ticker symbols like “OMXC25” or “OMXC25.CO,” the index is frequently mentioned in financial news outlets and is readily available to investors and traders worldwide.

Benchmark for equity funds

The performance of the top 25 companies listed on the Copenhagen Stock Exchange (Nasdaq Copenhagen) is represented by the OMXC 25 index, but it does not fully represent the size of the Danish equity market. Because of this, investors seeking a more thorough representation of the Danish market may want to think about other, wider market indices, like the OMXC 25 or the OMXC All-Share.

The 25 most active and liquid companies listed on Nasdaq Copenhagen are included in the OMXC 25 index, which offers a more comprehensive view of the Danish market. The OMXC All-Share index, on the other hand, provides a more thorough overview of the Danish equity market as a whole and covers a wider range of companies, including both large and small caps. In order to accurately track their performance and align it with their investment goals in the Danish market, investors should carefully assess their investment objectives and strategies to determine the most appropriate benchmark index.

Financial products around the OMXC 25 index

With the help of the OMXC 25 index, these financial products give investors the chance to diversify their portfolios, get exposure to the Danish stock market, and perhaps even profit from market fluctuations.

Some of the main financial products associated with the OMXC 25 index are:

Exchange-Traded Funds (ETFs): ETFs, which are traded on stock exchanges like individual stocks, allow investors access to the OMXX 25 index. ETFs that track the performance of the OMXC 25 index, like the iShares OMXC 25 UCITS ETF and the Xact OMXC 25 ETF, give investors a broad view of the Danish market.
Options and Futures Contracts: Investors can purchase or sell the OMXC 25 index through options and futures contracts that are linked to the index at a specified price and future date. These derivative contracts can be used for hedging, speculation, and portfolio management, among other things.
Mutual Funds and Index Funds: A few mutual funds and index funds concentrate their investments in businesses that are part of the OMXX 25 index or seek to match its performance. With the help of these funds, investors now have an easy way to expose themselves to a diverse portfolio of Danish stocks.

Historical data for the OMXC 25 index

How to get the data?

The OMXC 25 index is the most common index used in finance, and historical data for the OMXC 25 index can be easily downloaded from the internet.

For example, you can download data for the OMXC 25 index from December 19, 2016 on Yahoo! Finance (the Yahoo! code for OMXC 25 index is ^OMXC25).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the OMXC 25 index.

Data file

The R program that you can download above allows you to download the data for the OMXC 25 index from the Yahoo! Finance website. The database starts on December 19, 2016. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the OMXC 25 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the OMXC 25 index.
Top of the file for the OMXC 25 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the OMXC 25 index

Figure 1 below gives the evolution of the OMXC 25 index from December 19, 2016 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the OMXC 25 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the OMXC 25 index returns from December 19, 2016 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the OMXC 25 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the OMXC 25 index

The R program that you can download above also allows you to compute summary statistics about the returns of the OMXC 25 index.

Table 4 below presents the following summary statistics estimated for the OMXC 25 index:

The mean
The standard deviation (the squared root of the variance)
The skewness
The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the OMXC 25 index.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the OMXC 25 index returns

Historical distribution

Figure 3 represents the historical distribution of the OMXC 25 index daily returns for the period from December 19, 2016 to December 30, 2022.

Figure 3. Historical distribution of the OMXC 25 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from December 19, 2016 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the OMXC 25 index daily returns with parameters estimated over the period from December 19, 2016 to December 30, 2022.

Figure 4. Gaussian distribution of the OMXC 25 index returns.
Gaussian distribution of the daily OMXC 25 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the OMXC 25 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the OMXC 25 index.

Table 5 below presents the following risk measures estimated for the OMXC 25 index:

The long-term volatility (the unconditional standard deviation estimated over the entire period)
The short-term volatility (the standard deviation estimated over the last three months)
The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the OMXC 25 index.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the OMXC 25 index while the study of the right tail is relevant for an investor holding a short position in the OMXC 25 index.

Why should I be interested in this post?

Students can gain a thorough understanding of industry dynamics, market competition, and the interplay of various factors that affect business success in Denmark by studying the OMXC 25 index. Investors can compare the performance of their portfolios to that of the larger Danish stock market using the OMXC 25 index as a benchmark. In addition to reflecting investor sentiment toward Denmark’s biggest and most actively traded companies, it offers a snapshot of the market’s health.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

About the OMXC 25 index

Nasdaq Index Description

Capital.com What is the OMXC20 index?

Data

Yahoo! Finance

Yahoo! Finance Data for the OMXC 25 index

About the author

The article was written in June 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The BEL 20 index

June 6, 2023 by Nithisha CHALLA

The BEL 20 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the BEL 20 index representing the Belgian equity market and details its characteristics.

The BEL 20 index

The top 20 companies listed on Euronext Brussels, Belgium’s main stock exchange, make up the BEL 20 index, a stock market index that measures performance. The BEL 20 index was created in 1991, and Euronext oversees its operation. The market capitalization, liquidity, and sector representation of the companies chosen for the index are taken into consideration.

The market capitalization of each stock determines its weight in the BEL 20 index, which is a capitalization-weighted index. To guarantee that the index continues to be a trustworthy representation of the Belgian equity market, it is rebalanced four times per year.

With the widely used ticker symbol “BEL20” in the financial sector, investors and traders can access the BEL 20 index through various financial news sources and trading platforms. The BEL 20 index is a useful tool for investors and financial professionals because it can give important insights into the performance of the Belgian economy and its best-performing companies.

Table 1 below gives the Top 10 stocks in the BEL 20 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the BEL 20 index.

Source: computation by the author (data: Yahoo! Finance website).

Table 2 below gives the sector representation of the BEL 20 index in terms of number of stocks and market capitalization as of January 31, 2023.

Table 2. Sector representation in the BEL 20 index.

Source: computation by the author (data: Yahoo! Finance website).

Calculation of the BEL 20 index value

The performance of the 20 largest and most actively traded companies listed on the Brussels Stock Exchange (Euronext Brussels) in Belgium is reflected in the BEL 20 index, which is a float-adjusted market-capitalization-weighted index. The Belgian Association of Financial Analysts (ABAF-BVFA), which chooses the companies to be included in the index based on their liquidity, market capitalization, and free float, reviews the index on a quarterly basis.

The BEL 20 is rebalanced quarterly, taking into account any changes in the market capitalization of the constituent companies, to make sure the index accurately reflects the performance of the Belgian stock market.

The formula to compute the BEL 20 index is given by

Float Adjusted Market Capitalization Index value

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula

Float Adjusted Market Capitalization Weighted Index Weight

Use of the BEL 20 index in asset management

Investors frequently use the BEL 20 index as a benchmark to assess the performance of their investment portfolios in relation to the larger Belgian stock market.

Investors and analysts can learn more about the performance of the Belgian economy and its major sectors—such as financial services, consumer goods, and energy—by examining the changes in the BEL 20 index. Investors and traders can access the index using ticker symbols like “BEL20” or “BEL20.BR” and it is frequently covered in financial news outlets. Investors should take into account other indexes and benchmarks for a more thorough evaluation of the Belgian market, however, as the BEL 20 index does not cover all industries and sectors in Belgium.

Benchmark for equity funds

For equity funds investing across the board in the Belgian market, the BEL 20 index may not always be the best benchmark. This is due to the fact that the BEL 20 index does not account for the entire Belgian equity market; rather, it only tracks the performance of the top 20 companies listed on Euronext Brussels. Investors may need to take into account other broader market indices, such as the BEL Mid, which includes the 60 next most significant listed companies after the BEL 20, or the BEL Small, which includes the smallest companies listed on Euronext Brussels, in order to obtain a more complete representation of the Belgian market. Investors should therefore assess their investment goals and plans before choosing the appropriate benchmark indices.

Financial products around the BEL 20 index

The performance of the businesses that make up the BEL 20 index is the main objective of these products. Several financial products follow the BEL 20 index, including:

Exchange-Traded Funds: ETFs that track the BEL 20 index include the Lyxor UCITS Bel 20 ETF and the iShares Bel 20 UCITS ETF
Index funds: The Candriam Equities Belgium Index and the BNP Paribas B Fund Belgium Index are examples of index funds that track the performance of the Bel 20 index

These financial products allow investors to follow the performance of the top 20 companies listed on the Euronext Brussels exchange as well as gain exposure to the Belgian equity market. These financial products could produce returns based on the performance of the Belgian equity market and assist investors in diversifying their portfolios.

Historical data for the BEL 20 index

How to get the data?

The BEL 20 index is the most common index used in finance, and historical data for the BEL 20 index can be easily downloaded from the internet.

For example, you can download data for the BEL 20 index from January 3, 1984 on Yahoo! Finance (the Yahoo! code for BEL 20 index is ^BFX).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the BEL 20 index.

Data file

The R program that you can download above allows you to download the data for the BEL 20 index from the Yahoo! Finance website. The database starts on January 3, 1984. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the BEL 20 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the BEL 20 index.
Top of the file for the BEL 20 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the BEL 20 index

Figure 1 below gives the evolution of the BEL 20 index from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the BEL 20 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the BEL 20 index returns from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the BEL 20 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the BEL 20 index

The R program that you can download above also allows you to compute summary statistics about the returns of the BEL 20 index.

Table 4 below presents the following summary statistics estimated for the BEL 20 index:

The mean
The standard deviation (the squared root of the variance)
The skewness
The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the BEL 20 index.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the BEL 20 index returns

Historical distribution

Figure 3 represents the historical distribution of the BEL 20 index daily returns for the period from January 3, 1984 to December 30, 2022.

Figure 3. Historical distribution of the BEL 20 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from January 3, 1984 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the BEL 20 index daily returns with parameters estimated over the period from January 3, 1984 to December 30, 2022.

Figure 4. Gaussian distribution of the BEL 20 index returns.
Gaussian distribution of the daily BEL 20 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the BEL 20 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the BEL 20 index.

Table 5 below presents the following risk measures estimated for the BEL 20 index:

The long-term volatility (the unconditional standard deviation estimated over the entire period)
The short-term volatility (the standard deviation estimated over the last three months)
The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the BEL 20 index.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the BEL 20 index while the study of the right tail is relevant for an investor holding a short position in the BEL 20 index.

Why should I be interested in this post?

By analyzing the companies in the BEL 20 index, students can gain an understanding of how these industries operate and the factors that influence their success. For example, students can explore how regulations affect the financial services industry, how innovation drives growth in the pharmaceutical sector, and how geopolitical events impact energy markets. This knowledge can be particularly useful for those pursuing careers in finance, economics, or business.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

About the BEL 20 index

Wikipedia What is the BEL 20 index

Currency BEL 20 index explained

Trading economics About Belgium Stock Market Index BEL20

Data

Yahoo! Finance

Yahoo! Finance Data for the BEL 20 index

About the author

The article was written in June 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The IBEX 35 index

June 6, 2023 by Nithisha CHALLA

The IBEX 35 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the IBEX 35 index representing the Spanish equity market and details its characteristics.

The IBEX 35 index

The Bolsa de Madrid’s benchmark stock market index, the IBEX 35 index, is regarded as Spain’s primary stock exchange. The company that runs the Spanish stock exchanges, Bolsas y Mercados Espaoles (BME), which was founded on January 14, 1992, is in charge of managing it.

The 35 most liquid and well-capitalized companies traded on the Bolsa de Madrid make up the index. Based on trading volume, liquidity, and free-float market capitalization, the companies listed are chosen. The index includes businesses from a wide range of industries, including consumer goods, energy, finance, and telecommunications.

The IBEX 35 index is a free-float market capitalization-weighted index, which means that the index’s weights are based on market capitalization and are float-adjusted for each stock. This makes sure that the movements of the index are more influenced by larger companies than by smaller ones.

The IBEX 35 index is widely represented on trading platforms and financial websites, like other significant stock market indices. The performance of the Spanish economy and the overall health of the European Union are closely watched by investors and analysts around the world.

The ticker symbol used in the financial industry for the IBEX 35 index is “IBEX”.

Table 1 below gives the Top 10 stocks in the IBEX 35 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the IBEX 35 index.

Source: computation by the author (data: Yahoo! Finance financial website).

Table 2 below gives the sector representation of the IBEX 35 index in terms of number of stocks and market capitalization as of January 31, 2023.

Table 2. Sector representation in the IBEX 35 index.

Source: computation by the author (data: Yahoo! Finance website).

Calculation of the IBEX 35 index value

As a free-float market-capitalization-weighted index that is float-adjusted, the IBEX 35 index is calculated by taking into account the market capitalization of each of the companies that make up the index. To ensure that the index accurately captures the performance of the Spanish stock market, Bolsas y Mercados Espaoles (BME), the Spanish stock exchange, reviews and rebalances the index twice a year. The stocks that will be included in the index are chosen by the Technical Advisory Committee of the BME, which takes into account elements like liquidity, market capitalization, and trading volume.

The formula to compute the IBEX 35 index is given by

Float Adjusted Market Capitalization Index value

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by

Float Adjusted Market Capitalization Weighted Index Weight

Use of the IBEX 35 index in asset management

The IBEX 35 index serves as a benchmark for assessing the performance of the Spanish stock market. Because it is a widely used indicator of the performance of the Spanish stock market, it can help investors with important asset management tasks like passive investments, evaluating corporate risk, asset allocation, portfolio management, and so forth. However, the performance of all markets or sectors is not accurately reflected by the IBEX 35 index, which only includes the 35 Spanish stocks with the highest level of liquidity. Therefore, when evaluating the performance of the Spanish equity market, investors should also consider other indices like the FTSE Spain Index and the MSCI Spain Index.

Benchmark for equity funds

Investors frequently use the IBEX 35 index as a benchmark. When using the IBEX 35 index as a benchmark for equity funds in Spain, it is important to remember that it only includes 35 of the largest and most popularly traded companies listed on the Spanish stock exchange. As a result, it might not accurately represent the whole Spanish market, as there are many small and mid-cap companies in Spain that are not represented by the index. The benchmark index to be used will ultimately depend on the specific investment objectives and strategies of the fund in question.

Financial products around the IBEX 35 index

Through the IBEX 35 index, these financial products give investors access to the Spanish stock market, portfolio diversification, and the potential to profit from market fluctuations.

Some of the main financial products related to the IBEX 35 index are:

Exchange-Traded Funds (ETFs): Through ETFs, which are traded like stocks, investors can gain access to the IBEX 35 index. ETFs that follow the Ibex 35 index include the iShares Ibex 35 UCITS ETF and the Amundi ETF Ibex 35.
Options and Futures Contracts: Investors can use options and futures contracts to buy or sell the IBEX 35 index at a predetermined price and date in the future. This is typically done to generate income through trading strategies, hedge against market volatility, or predict the index’s performance.
Mutual Funds and Index Funds: Some mutual funds and index funds concentrate on investing in businesses that are part of the IBEX 35 index or seek to replicate the performance of the index by acquiring the same stocks that comprise the index.

Historical data for the IBEX 35 index

How to get the data?

The IBEX 35 index is the most common index used in finance, and historical data for the IBEX 35 index can be easily downloaded from the internet.

For example, you can download data for the IBEX 35 index from July 12, 1993 on Yahoo! Finance (the Yahoo! code for IBEX 35 index is ^IBEX).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the IBEX 35 index.

Data file

The R program that you can download above allows you to download the data for the IBEX 35 index from the Yahoo! Finance website. The database starts on July 12, 1993. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the IBEX 35 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the IBEX 35 index.
Top of the file for the IBEX 35 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the IBEX 35 index

Figure 1 below gives the evolution of the IBEX 35 index from July 12, 1993 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the IBEX 35 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the IBEX 35 index returns from July 12, 1993 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the IBEX 35 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the IBEX 35 index

The R program that you can download above also allows you to compute summary statistics about the returns of the IBEX 35 index.

Table 4 below presents the following summary statistics estimated for the IBEX 35 index:

The mean
The standard deviation (the squared root of the variance)
The skewness
The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the IBEX 35 index.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the IBEX 35 index returns

Historical distribution

Figure 3 represents the historical distribution of the IBEX 35 index daily returns for the period from July 12, 1993 to December 30, 2022.

Figure 3. Historical distribution of the IBEX 35 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from July 12, 1993 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the IBEX 35 index daily returns with parameters estimated over the period from July 12, 1993 to December 30, 2022.

Figure 4. Gaussian distribution of the IBEX 35 index returns.
Gaussian distribution of the daily IBEX 35 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the IBEX 35 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the IBEX 35 index.

Table 5 below presents the following risk measures estimated for the IBEX 35 index:

The long-term volatility (the unconditional standard deviation estimated over the entire period)
The short-term volatility (the standard deviation estimated over the last three months)
The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the IBEX 35 index.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the IBEX 35 index while the study of the right tail is relevant for an investor holding a short position in theIBEX 35 index.

Why should I be interested in this post?

Students can gain useful knowledge about the Spanish stock market and its major sectors by looking at the IBEX 35 index. These firms represent a wide range of industries, including consumer goods, energy, finance, and telecommunications, making the index a useful benchmark for the Spanish economy. Students can learn how industries function, how competition affects the market, and what elements contribute to business success in Spain by examining the performance of the companies included in the index.

Furthermore, investors can use financial products linked to the IBEX 35 index, such as exchange-traded funds (ETFs), futures, and options contracts, to access the Spanish market and potentially generate returns. By understanding the dynamics of the IBEX 35 index and the Spanish economy, students can develop valuable skills for careers in investment banking, portfolio management, and corporate finance.

Useful resources

About the IBEX 35 index

Wikipedia What is the IBEX 35 index

AVA trade An Overview of Spain’s Financial Engine – IBEX 35

DailyFX What is the IBEX 35 Index and what influences its price?

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance

Yahoo! Finance Data for the IBEX 35 index

About the author

The article was written in June 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

Decoding Business Performance: The Top Line, The Line, and The Bottom Line

June 3, 2023 by Isaac ALLIALI

Decoding Business Performance: The Top Line, The Line, and The Bottom Line

In this article, Isaac ALLIALI (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023) decodes the business performance by analyzing the top line, the line, and the bottom line.

Introduction

In the realm of finance and business, terms like “top line,” “the line,” and “bottom line” often dominate discussions. But what do they really mean, and why are they so important in evaluating a company’s financial health? This article aims to elucidate these key financial terms and their relevance to business performance assessment.

The Top Line

The “top line” refers to a company’s gross revenue or sales, so named because it appears at the top of a company’s income statement. It reflects the total revenue earned from the sale of goods or services before deducting any costs or expenses. This figure is crucial as it indicates the company’s ability to sell its products or services, which is fundamental to its business operations.

The strategies for increasing the top line generally focus on enhancing sales through marketing efforts, pricing strategies, product development, or expanding into new markets. While it may seem that a growing top line (revenue) is indicative of profitability, it is important to recognize that this metric alone does not consider the expenses associated with generating that revenue. In other words, the increase in revenue does not guarantee increased profitability. It is crucial for investors to understand that a company’s top line growth does not always align with its profitability.

For instance, if the cost of producing goods or services is rising faster than sales, profits might be shrinking despite increased revenues.

The Line

While “the line” is a less commonly used term in comparison to the “top line” and “bottom line”, it is often used to refer to the “break-even line.” The break-even line represents a point where total costs (including both fixed and variable costs) are equal to total revenue.

At this juncture, the company isn’t making a profit, but it isn’t incurring a loss either. Understanding the break-even point is essential for businesses because it provides a clear target to cover costs and start making profits.

Knowing when a company will hit its break-even point can help investors understand when it might start turning a profit. In addition, a company with a lower break-even point can withstand market fluctuations better, representing a potentially less risky investment.

The Bottom Line

The “bottom line” is arguably the most significant figure on an income statement, representing the company’s net income. It’s the residue left after deducting all expenses, including cost of goods sold (COGS), operating expenses, interest payments, and taxes from the top line. This term gets its name because net income is listed at the bottom of the income statement.

The bottom line demonstrates a company’s profitability, and strategies to improve it usually focus on enhancing gross revenue or reducing costs. Shareholders closely monitor the bottom line because it directly affects earnings per share and dividends. However, solely focusing on improving the bottom line can sometimes lead to unsustainable strategies like excessive cost-cutting.

However, investors should also be aware that an increasing bottom line can sometimes be achieved through aggressive cost-cutting, which may not be sustainable in the long run. It’s important to scrutinize the sources of bottom-line growth: Is it due to increased sales, improved operational efficiency, or simply cost-cutting?

Conclusion

Understanding the terms “top line,” “the line,” and “bottom line” is crucial for interpreting a company’s financial performance. While the top line provides insight into sales performance and the bottom line into profitability, it’s the intricate story that unfolds between these two lines that often holds the most valuable insights for sustainable growth and profitability. As such, a holistic view of a company’s financial health should consider all these aspects.

By focusing on each line in tandem, companies can better navigate their path to profitability, creating strategies that stimulate sales growth (top line), manage costs effectively (the line), and ultimately drive profit (bottom line). However, these metrics should not be used in isolation. Investors should use them in conjunction with other financial ratios and indicators to make informed decisions.

By aligning their strategies to promote sales growth (top line) and efficient cost management practices (the line), companies can navigate their path to profitability. The aim is to strike a balance between revenue generation and cost control to drive profitability (bottom line). However, it’s important to note that these metrics should not be evaluated in isolation. Investors should consider utilizing other financial ratios and indicators to gain a comprehensive understanding of a company’s financial health. These may include profitability ratios (such as gross profit margin, operating margin, and net profit margin), liquidity ratios (like current ratio and quick ratio), debt ratios (such as debt-to-equity ratio and interest coverage ratio), and efficiency ratios (like inventory turnover and receivables turnover). Evaluating these indicators collectively provides a more comprehensive assessment of a company’s performance and prospects, empowering investors to make informed investment decisions. Each line tells a different part of the company’s financial story, and understanding the interplay between them is crucial for investment decision-making.

Illustration

Income statement of Ford.
The Top Line, The Line, and The Bottom Line
Source: the company.

Why should I be interested in this post?

These concepts form the foundation of financial analysis and provide valuable insights into a company’s financial performance. Understanding the top line, which represents revenue or sales, is crucial as it demonstrates a company’s ability to generate income and sustain growth. The bottom line, which reflects the net income or profit after deducting expenses, taxes, and interest, provides a measure of overall profitability. By delving into the line, which encompasses various expenses impacting profitability, finance students can gain a comprehensive understanding of financial statements and develop the analytical skills necessary to evaluate a company’s financial health, make informed investment decisions, and contribute to effective financial strategies. This knowledge is highly applicable in various finance-related roles and is instrumental in navigating the complexities of the business world.

About the author

The article was written in June 2023 by Isaac ALLIALI (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023).

Understanding the Gordon-Shapiro Dividend Discount Model: A Key Tool in Valuation

January 30, 2024June 2, 2023 by Isaac ALLIALI

Understanding the Gordon-Shapiro Dividend Discount Model: A Key Tool in Valuation

In this article, Isaac ALLIALI (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023) explains about the Gordon-Shapiro Dividend Discount Model, which is a key tool in valuation.

Introduction

The Gordon-Shapiro Dividend Discount Model, also known as the Gordon-Shapiro formula and the Gordon Growth Model, is a central tenet in finance. It provides investors and financial analysts a simple tool to value a company based on its future dividends that are expected to remain at a constant growth rate. This model was named after economists Myron J. Gordon and Eli Shapiro, who developed it.

The Gordon-Shapiro formula

The Gordon-Shapiro formula is articulated through a relatively simple equation:

Gordon Shapiro formula

where:

V stands for the value of the stock.
DIV1 represents the expected dividend in the next period.
k is the investor’s required rate of return.
g is the constant growth rate of dividends.

This formula is premised on the idea that a company’s stock is worth the present value of all its future dividends.

Proof of the Gordon-Shapiro formula

To understand the derivation of the formula, let us consider a perpetuity model for valuing stocks. In a perpetuity model, the value of an asset is determined by the discounted value of its future cash flows. In the case of stocks, dividends represent the cash flows received by investors (shareholders or stockholders).

Assuming that the company pays a constant dividend indefinitely, the present value of the future dividends can be expressed as follows:

Gordon Shapiro formula

where DIV₁, DIV₂, DIV₃ and so on, represent the expected dividends in subsequent periods.

To simplify the formula, we assume that the dividend grows at a constant rate (g). This means that each subsequent dividend can be expressed as a multiple of the previous dividend:

Gordon Shapiro formula

Substituting these dividend expressions into the perpetuity formula, we have:

Gordon Shapiro formula

Inside the parentheses, we recognize an infinite geometric series with a ratio q equal to (1+g)/(1+k) for the geometric sequence.

Gordon Shapiro formula

The sum of an infinite geometric series denoted by S with a ratio q is equal to 1/(1-q). Applied to the case above, we obtain:

Gordon Shapiro formula

This leads to the Gordon Shapiro formula:

Gordon Shapiro formula

Simplifying further:

Gordon Shapiro formula

Therefore, the Gordon-Shapiro formula for estimating the intrinsic value of a stock is derived.

Assumptions of the Gordon Growth Model

The Gordon-Shapiro Dividend Discount Model is based on several key assumptions:

Constant Growth Rate: the model assumes that dividends grow at a constant rate indefinitely.

Required Rate of Return: the required rate of return exceeds the dividend growth rate. This condition is necessary for the formula to work.

Dividends: the company is expected to distribute dividends.

While these assumptions may not hold in all cases, they offer a starting point for the valuation process.

Applicability of the Gordon Growth Model

The Gordon Growth Model is especially useful in certain scenarios. For example, it is an excellent tool when assessing companies with stable growth rates, such as utility companies or large, mature firms.

However, the model has limitations when used for companies that don’t pay dividends or those with a dividend growth rate that is not consistent. High-growth companies, for instance, reinvest their profits for expansion rather than paying dividends. Similarly, companies facing fluctuating growth rates may present challenges for the model’s assumptions.

Example

After researching Pfizer’s data, we assume that this company pays an annual dividend per share (DPS) of $0.40. The required rate of return (k) for the company’s stock 9,16% was computed with the CAPM Model under the following assumptions: (Risk free rate of return= 4,73%; Beta of Pfizer stock is 0,62 and Market rate of return =11,88%), and the expected growth rate of dividends (g) is 6,40%.

Using the Gordon Shapiro formula:

Gordon Shapiro formula

In this example, based on the given assumptions, the Gordon Shapiro model estimates the intrinsic value (V0) of Pfizer’s stock to be $14.48 per share. The current market price of Pfizer’s stock ($37,60) is significantly higher than the estimated intrinsic value, it could suggest that the stock is potentially overvalued. This may indicate a cautionary signal for investors, as it implies that the stock’s market price may not be justified by the projected dividends and required rate of return. It’s important to note that the Gordon Shapiro model is a simplified valuation tool and relies on various assumptions. The actual value of a stock is influenced by numerous factors, including market conditions, company performance, industry trends, and investor sentiment. Investors should conduct further research, analyze additional factors, and seek professional advice before making investment decisions based solely on the findings of the Gordon Shapiro model or any other valuation model.

Conclusion

Despite its limitations, the Gordon-Shapiro Dividend Discount Model remains a valuable tool in financial analysis and investment decision-making. Its simplicity and focus on dividends make it an attractive model for investors, especially when applied appropriately and in the right context. Investors and financial analysts alike should understand this model as part of their toolkit for assessing a company’s inherent value.

Useful resources

SimTrade course Financial analysis

Gordon, Myron J., and Eli Shapiro (1956) “Capital Equipment Analysis: The Required Rate of Profit.” Management Science, 3(1): 102-110.

About the author

The article was written in June 2023 by Isaac ALLIALI (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023).

The Psychology of Trading

May 29, 2023 by Theo SCHWERTLE

The Psychology of Trading

In this article, Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2023) explains how behavioral biases can influence trading of market aprticiapnts.

Behavioral biases of investors

In complex decision environments, people use basic judgements and preferences to simplify the scenario rather than adhere to a strictly rational approach. This use of mental shortcuts is called heuristics, which are quick and instinctively appealing but may result in poor outcomes (Tversky and Kahneman, 1974). The traditional financial theory (based on expected utility theory) assumes that people are rational agents. In contrast to traditional financial theory, behavioral theories argue that people are generally risk-averse with a skewed view of probability (Kahneman and Tversky, 1979). Some common behavioral biases that have been identified in the literature on investment decisions include overconfidence, the disposition effect and herding behavior.

Prospect Theory

We start with the two main drivers of irrationality: value perception and probability perception.

Value perception. The value function proposed by Kahneman and Tversky (1979) is characterized by the following features. First, it is determined based on departures from a reference point. Second, it typically has a downward, concave slope for gains and an upward, convex slope for losses. This suggests that individuals perceive losses as more painful gains as shown in Figure 1.

Figure 1. Perceived value function.

Source: Kahneman and Tversky (1979).

Probability perception. Individuals tend to assign a lower probability value to outcomes that are more likely to occur and, a higher probability value to outcomes that are less likely to occur as shown in Figure 2.

Figure 2. Perceived probability.

Source: Kahneman and Tversky (1979).

Overconfidence

Overconfidence manifests as an inclination to have an irrationally excessive level of trust in one’s own abilities and opinions and has been thoroughly investigated across many fields (Fischhoff et al., 1977).

Gervais and Odean (2001) explore how overconfidence develops as a result of a dynamic change in beliefs about one’s ability after observing successes and failures. Successful traders tend to be overconfident due to attributing too much credit to their own ability. They showed that overconfidence is highest among inexperienced traders, as proper self-assessment only develops over time. This leads to suboptimal behavior, such as increased trading volume and volatility, lower expected profits, and poor information utilization (Statman et al., 2006).

Ekholm and Pasternack (2007) investigate the link between overconfidence and investor size.
They show that larger investors are less overconfident than small investors. They also show that larger investors, on average, react more positively to good news and more negatively to bad news than smaller investors. Evidence suggests that smaller, more overconfident investors have worse performance following negative news (Ekholm and Pasternack, 2007).

Grinblatt and Keloharju (2009) argue that sensations seekers (people receiving more speeding tickets) and those who showed more overconfidence as measured by a psychological assessment traded more than the average, even after controlling for other factors that might explain trading activity like age, income and gender. Similarly, individual investors tend to buy stocks that have recently caught their attention, like stocks with high trading volume, extreme one-day returns, or those in the news, whereas institutional investors, especially those who follow a value strategy, do not (Barber and Odean, 2007). These results are confirmed by Barber et al. (2022) as Robinhood users, which are, as evidence suggests, less experienced traders, trade substantially more high-attention stocks.

Additionally, men are more prone to overconfidence than women, particularly in male-dominated industries like finance. Thus, men trade more than women and perform worse in terms of returns. Male investors not only engage in more frequent trading but, compared to female investors, also hold larger and less diversified portfolios (Barber & Odean, 2001; Lepone et al., 2022).

Why should I be interested in this post?

This post explores heuristics and behavioral biases in decision-making, particularly in the context of investment decisions. Overconfidence can lead to poor outcomes. Additionally, it touches on gender differences, with men being more prone to overconfidence and engaging in more frequent trading. By understanding these biases, readers can gain insights into human behavior, make more informed investment decisions, and explore the impact of gender on financial outcomes. Overall, this post offers valuable insights into decision-making processes and their implications.

Useful resources

Barber, B.M. and Odean, T. (2007) All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors Review of Financial Studies 21(2):785–818.

Barber, B.M. and Odean, T. (2001) Boys will be Boys: Gender, Overconfidence, and Common Stock Investment The Quarterly Journal of Economics 116(1):261–292.

Ekholm, A. and Pasternack, D. (2007) Overconfidence and Investor Size European Financial Management.

Fischhoff, B., Slovic, P. and Lichtenstein, S. (1977) Knowing with certainty: The appropriateness of extreme confidence. Journal of Experimental Psychology: Human Perception and Performance 3(4):552–564.

Gervais, S. and Odean, T. (2001) Learning to Be Overconfident Review of Financial Studies 14(1):1–27.

Grinblatt, M. and Keloharju, M. (2009) Sensation Seeking, Overconfidence, and Trading Activity The Journal of Finance 64(2):549–578.

Kahneman, D. and Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk Econometrica 47(2): 263.

Lepone, G., Westerholm, J. and Wright, D. (2022) Speculative trading preferences of retail investor birth cohorts Accounting & Finance.

Statman, M., Thorley, S. and Vorkink, K. (2006) Investor Overconfidence and Trading Volume Review of Financial Studies 19(4):1531–1565.

Tversky, A. and Kahneman, D. (1974) Judgment under Uncertainty: Heuristics and Biases Science 185(4157):1124–1131.

About the author

The article was written in May 2023 by Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2018-2023).

Key participants in the Private Equity ecosystem

May 18, 2023 by Matisse FOY

Key participants in the Private Equity ecosystem

In this article, Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023) explains who the key participants in Private Equity (PE) are, and what are their role in the PE ecosystem.

Private Equity is an increasingly important model of financing for companies at different scales. Whether you’re simply interested in the subject or want to find a professional experience, here is a list of the main participants in the PE ecosystem and their function.

Key participants in the Private Equity ecosystem

Source: production by the author

A glossary of the participants

Private Equity funds

PE funds are the central actors in the private equity ecosystem, pooling capital from various sources (mainly from Limited Partners and Investment Banks) and invest this money in private companies, meaning companies whose shares cannot be freely bought and sold on the stock market.

The employees of PE funds are responsible for sourcing, evaluating, and managing investments in “Portfolio Companies”.

Their objective is to enhance the performance of those Portfolio Companies. By doing so, they aim to sell these firms later and generate profit. This profit is primarily derived from the investment capital provided by their investors, from which they take a percentage as their fee.

General Partners (GPs)

These are the managers of the PE fund who make the investment decisions. They have a fiduciary duty to act in the best interest of the LPs.

GPs are typically compensated through a management fee, which is a fixed annual fee for the fund’s operation, and a performance fee (also known as “carry”), which is a percentage of the profits of the fund.

Limited Partners (LP)

Limited Partners are the investors in a PE fund. They include institutional investors like pension funds, university endowments (like Harvard University endowment), insurance companies (e.g., AXA, Allianz), and sovereign wealth funds, as well as high net worth individuals.

Limited Partners provide the capital that the PE funds invest and expect a return on their investment.

Portfolio Companies

Portfolio Companies are the companies in which PE funds invest. They are often in need of capital for growth, restructuring, or as part of a strategy to transition the company from public to private.

The goal of PE funds is to take a share in these companies, improve their performance and sell them for a profit.

Investment Banks

Investment Banks often play a crucial role in the PE ecosystem, especially with regards to the acquisition and sale of portfolio companies by PE funds. They can help PE funds identify potential investment opportunities, facilitate transactions, and provide financing by leveraging Limited Partners’ equity. Moreover, they can help portfolio companies go public when they are sold.

Law Firms and Consultants

These professional service providers support PE funds throughout the investment process:

Law firms help with legal aspects of transactions, including drafting and reviewing contracts, to ensure compliance with relevant laws and regulations, and advising on the structure of deals to minimize legal risks and tax liabilities.
Consultants, on the other hand, assist with due diligence and the development of strategies for improving the performance of portfolio companies. They might also be delegated the sourcing and contact with portfolio companies by PE funds.

Regulators

Regulators oversee and govern the operations of PE funds. They aim to protect the interests of investors and the integrity of the financial markets, in order for the local environment to be as attractive to invest in as possible.

Why should I be interested in this post?

Private Equity is a wide ecosystem. Knowing about its different participants is very important when deciding to work in one of them, in order to understand their importance (who knows, maybe you will be asked questions about these actors will be asked to you in your next interview).

Useful resources

The Financial Times Private Equity

Wall Street Journal Private Equity

Coursera’s MOOC Private Equity and Venture Capital

About the author

The article was written in May 2023 by Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023).

The DAX 30 index

May 14, 2023 by Nithisha CHALLA

The DAX 30 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the DAX 30 index and details its characteristics.

The DAX 30 index

The largest and most liquid 30 publicly traded German companies are represented by the DAX 30 index. This index was established by the Frankfurt Stock Exchange on July 1, 1988. “Deutscher Aktienindex” or the German stock index in English, is abbreviated as DAX. Deutsche Boerse AG, which also runs the Frankfurt Stock Exchange, is in charge of managing the DAX 30.

The choice of the companies for the DAX index is based on a number of variables, such as trading volume, market capitalization, and liquidity. The Deutsche Boerse Index Commission regularly modifies and reviews the index’s composition, ensuring that DAX 30 accurately captures the overall performance of the German stock market.

The DAX 30 is a free float market capitalization-weighted index, which means that each company’s weight in the index is based on the calculation of its market capitalization. The performance of the German stock market is measured against the DAX 30, which is closely monitored by traders and investors worldwide. Investors and traders wishing to follow the performance of the German stock market can easily access the index as it is published and distributed in real-time by several financial news sources.

The ticker symbol “DAX” is used in trading platforms and financial websites to identify the DAX 30.

Table 1 below gives the Top 10 stocks in the DAX 30 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the DAX 30 index.

Source: computation by the author (data: Yahoo! Finance website).

Calculation of the DAX 30 index value

The performance of the 30 largest and busiest German companies listed on Frankfurt Stock Exchange is reflected in the DAX 30, a blue-chip stock market index. A free-float market-capitalization-weighted methodology is utilized to calculate the index, which means that each company’s weight in the index is determined by its market capitalization adjusted for the shares that are actually traded in the secondary market (float).

The formula to compute the DAX 30 index is given by

Float Adjusted Market Capitalization Index value

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula

Float Adjusted Market Capitalization Weighted Index Weight

Use of the DAX 30 index in asset management

Investors can examine the sector weightings and geographic exposure of the index to gain insights into performance of the German economy to identify potential opportunities and risks in particular industries or regions. Asset managers compare performance of their equity portfolios to the performance of the complete market using the DAX 30 as the benchmark. Multiple investment products, including exchange-traded funds (ETFs), options, and futures contracts, all have the index as the starting point.

Benchmark for equity funds

One of the highly significant indices in Europe, the DAX 30 serves as standard for the overall performance of German stock market. The businesses represent numerous industries, including those in the automotive, financial, healthcare, technology, and retail sectors. Asset managers and investors use the DAX 30 as the benchmark to compare performance of their portfolios to that of the market as a whole. It is used as gauge of investor sentiment toward the nation’s businesses and financial markets as well as a barometer for the health of the German economy.

Financial products around the DAX 30 index

There are various financial products available that allow investors to gain exposure to German equity market through the DAX 30 index.

ETFs are investment funds traded on stock exchanges which are designed to track the performance of an index. Some of the ETFs that track the DAX 30 index include the iShares DAX UCITS and the X Trackers DAX UCITS.
Index funds are designed to track the performance of the index. Examples of the index funds based on the DAX 30 index include the DWS Deutschland Index Fund and the Allianz DAX Index Fund.
Futures and options contracts based on the DAX 30 index provide investors with ability to speculate on the future performance of the index. Eurex offers futures and options contracts based on the DAX 30 index.
Certificates are investment products allowing investors to gain exposure to the DAX 30 index. Commerzbank offers a range of certificates linked to the DAX 30 index, such as the ComStage DAX UCITS ETF.

Overall, these financial products offer investors the ability to diversify their portfolios and gain exposure to German equity market, as well as potentially benefit from the performance of the DAX 30 index.

Historical data for the DAX 30 index

How to get the data?

The DAX 30 index is the most common index used in finance, and historical data for the DAX 30 index can be easily downloaded from the internet.

For example, you can download data for the DAX 30 index from December 30, 1987 on Yahoo! Finance (the Yahoo! code for DAX 30 index is ^GDAXI).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the DAX 30 index.

Data file

The R program that you can download above allows you to download the data for the DAX 30 index from the Yahoo! Finance website. The database starts on December 30, 1987. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the DAX 30 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the DAX 30 index.
Top of the file for the DAX 30 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the DAX 30 index

Figure 1 below gives the evolution of the DAX 30 index from December 30, 1987 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the DAX 30 index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the DAX 30 index returns from December 30, 1987 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the DAX 30 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the DAX 30 index

The R program that you can download above also allows you to compute summary statistics about the returns of the DAX 30 index.

Table 4 below presents the following summary statistics estimated for the DAX 30 index:

The mean
The standard deviation (the squared root of the variance)
The skewness
The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the DAX 30 index.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the DAX 30 index returns

Historical distribution

Figure 3 represents the historical distribution of the DAX 30 index daily returns for the period from December 30, 1987 to December 30, 2022.

Figure 3. Historical distribution of the DAX 30 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from December 30, 1987 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the DAX 30 index daily returns with parameters estimated over the period from v to December 30, 2022.

Figure 4. Gaussian distribution of the DAX 30 index returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the DAX 30 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the DAX 30 index.

Table 5 below presents the following risk measures estimated for the DAX 30 index:

The long-term volatility (the unconditional standard deviation estimated over the entire period)
The short-term volatility (the standard deviation estimated over the last three months)
The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the DAX 30 index.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the DAX 30 index while the study of the right tail is relevant for an investor holding a short position in the DAX 30 index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the DAX 30 index. The index includes wide range of industries, including energy, finance, telecommunications, and consumer goods, and it covers the biggest and most liquid German companies. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Russia or interested in investing in German equities.

Individual investors can assess the performance of their own investments in the German equity market with the DAX 30 index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the DAX 30 index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Business

CFI DAX Stock Index Explained

Wikipedia An introduction to the DAX 30 index

Avatrade Trade the DAX index

Data

Yahoo! Finance

Yahoo! Finance Historical data for the DAX 30 index

About the author

The article was written in May 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The MOEX Russia index

May 14, 2023 by Nithisha CHALLA

The MOEX Russia index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the MOEX Russia index and details its characteristics.

The MOEX Russia index

The Moscow Exchange Russia Index (MOEX Russia Index) is market-capitalization-weighted index of the 50 biggest and most liquid companies listed on the Moscow Exchange. It was first presented in 1997 and serves as the benchmark index for the Russian stock market.

A wide range of sectors are covered by the MOEX Russia Index, including consumer goods, energy, finance, and telecommunications. By market capitalization, Gazprom, Sberbank, Lukoil, Novatek, and Tatneft were the top five index members as of September 2021.

The MOEX Russia Index is a market-capitalization-weighted index, which means that rather than using share price to determine a company’s weight in the index, it utilizes market capitalization. This enables it to depict the overall performance of the Russian equity market with greater accuracy.

Investors and asset managers frequently use the MOEX Russia Index as a benchmark to monitor the performance of the Russian equity market. ETFs and index funds are examples of financial products that are made to track the MOEX Russia Index.

The MOEX Russia Index has the ticker “IMOEX” in the financial sector.

Table 1 below gives the Top 10 stocks in the MOEX Russia index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the MOEX Russia index.

Source: computation by the author (data: Yahoo! Finance website).

Calculation of the MOEX Russia index value

As per the free-float methodology, which is used to calculate the MOEX Russia Index, each company’s weight in the index is determined by the percentage of its shares that are available for public trading rather than by its overall market capitalization. The goal of this methodology is to present a more accurate picture of the market value of each company.

The formula to compute the MOEX Russia is given by

Float Adjusted Market Capitalization Index value

Where I is the index value, k a given asset, K the number of assets in the index, P^k the market price of asset k, N^k the number of issued shares for asset k, F^k the float factor of asset k, and t the time of calculation of the index.

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula can be rewritten as

Float Adjusted Market Capitalization Weighted Index Weight

Use of the MOEX Russia index in asset management

For asset managers who make investments in the Russian equity market, the MOEX Russia index serves as a crucial benchmark. It is used as an exchange-traded fund (ETF) and Russian equity fund performance benchmark. The index can be used by investors to assess the performance of their portfolios and compare it to the performance of the complete market.

Benchmark for equity funds

Equity funds that invest in Russian companies use the MOEX Russia Index as a benchmark. The MOEX Russia index can also serve as the foundation for the investment products that track indices, like index funds and ETFs. These goods are made to follow the index’s performance and give buyers access to Russian equity market. Investors can gain broad market exposure through the purchase of these products without picking individual stocks.

Financial products around the MOEX Russia index

There are several financial products tracking the performance of the MOEX Russia Index, allowing investors to gain exposure to the Russian stock market.

ETFs are investment funds traded on the stock exchanges, designed to track performance of an index. There are several ETFs that track the MOEX Russia Index, such as the Xtrackers Russia UCITS and the VanEck Vectors Russia
Index funds are designed to track performance of an index. Index funds based on the MOEX Russia Index include the Sberbank Asset Management MOEX Russia Index Fund and the Raiffeisen Russia Equity Fund.
Futures and options contracts based on the MOEX Russia Index provide investors with the ability to speculate on the future performance of the index. For example, the Moscow Exchange offers futures contracts based on the MOEX Russia Index.
Certificates are investment products that allow investors to get exposure to the MOEX Russia Index. Société Générale offers a range of certificates linked to the MOEX Russia Index, such as the MOEX Russia Index Tracker Certificate.

Historical data for the MOEX Russia index

How to get the data?

The MOEX Russia index is the most common index used in finance, and historical data for the MOEX Russia index can be easily downloaded from the internet.

For example, you can download data for the MOEX Russia index from January 3, 1984 on Yahoo! Finance (the Yahoo! code for MOEX Russia index is IMOEX.ME).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the MOEX Russia index.

Data file

The R program that you can download above allows you to download the data for the MOEX Russia index from the Yahoo! Finance website. The database starts on January 3, 1984. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the MOEX Russia index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the MOEX Russia index.
Top of the file for the MOEX Russia index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the MOEX Russia index

Figure 1 below gives the evolution of the MOEX Russia index from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the MOEX Russia index.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the MOEX Russia index returns from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the MOEX Russia index returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the MOEX Russia index

The R program that you can download above also allows you to compute summary statistics about the returns of the MOEX Russia index.

Table 4 below presents the following summary statistics estimated for the MOEX Russia index:

The mean
The standard deviation (the squared root of the variance)
The skewness
The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the MOEX Russia index.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the MOEX Russia index returns

Historical distribution

Figure 3 represents the historical distribution of the MOEX Russia index daily returns for the period from January 3, 1984 to December 30, 2022.

Figure 3. Historical distribution of the MOEX Russia index returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from January 3, 1984 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the MOEX Russia index daily returns with parameters estimated over the period from January 3, 1984 to December 30, 2022.

Figure 4. Gaussian distribution of the MOEX Russia index returns.
Gaussian distribution of the daily MOEX Russia index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the MOEX Russia index returns

The R program that you can download above also allows you to compute risk measures about the returns of the MOEX Russia index.

Table 5 below presents the following risk measures estimated for the MOEX Russia index:

The long-term volatility (the unconditional standard deviation estimated over the entire period)
The short-term volatility (the standard deviation estimated over the last three months)
The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the MOEX Russia index.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the MOEX Russia index while the study of the right tail is relevant for an investor holding a short position in the MOEX Russia index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the MOEX Russia index. The index includes wide range of industries, including energy, finance, telecommunications, and consumer goods, and it covers the biggest and most liquid companies listed on the Moscow Exchange. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Russia or interested in investing in Russian equities.

Individual investors can assess the performance of their own investments in the Russian equity market with the MOEX Russia index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the MOEX Russia index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Business

wikipedia What is the MOEX Russia index?

Moex Everything about MOEX

Data

Yahoo! Finance

Yahoo! Finance MOEX Russia index

About the author

The article was written in May 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

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