Financial markets

Enhancing Financial Market Learning: The ‘Pair & Share’ Pedagogical Approach

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Enhancing Financial Market Learning: The ‘Pair & Share’ Pedagogical Approach

François LONGIN

In this article, Professor François LONGIN (ESSEC Business School, Finance Department) explains how enhance financial market learning with the ‘Pair & Share’ pedagogical approach.

The SimTrade course

The SimTrade course, offered at ESSEC Business School, is an innovative program designed to provide students with a hands-on understanding of financial markets. At its core, SimTrade combines theoretical knowledge with practical applications, allowing participants to engage in realistic market simulations. Students can experiment with trading strategies, analyze market reactions, and make decisions in a controlled environment, fostering a deeper comprehension of market dynamics and investor behavior.

The course is grounded in the belief that experiential learning is essential for mastering the complexities of finance. By bridging theory and practice, SimTrade empowers students to navigate the fast-paced world of financial markets with confidence and competence.

The ‘Pair & Share’ pedagogical approach

I describe below the “Pair & Share” pedagogical approach that I discovered during the Glocoll program at Harvard Business School. The “Pair & Share” sequence is organized in three steps:

Step 1: Think Individually

I ask participants to consider the question: “What are three key points about financial markets?” for one minute.

Step 2: Pair & Share

I ask participants to exchange their ideas in groups of two. Participant A explains to Participant B what he/she thinks is important about financial markets, and vice versa. I also informed them that in the next step, I will ask the question : What have you learned from your partner?

Step 3: Group Feedback

Insights are shared with the class, summarized into a mind map.

You will find below the mind about financial markets from the students in the course that I teach at ESSEC Business school (Bachelor of Business Administration (BBA), Master in Finance (MiF), and Master in Strategy and Management of International Business (SMIB)).

Please click on the image below to download the mind map of the Pair & Share exercise on financial markets.

Download the mind map of the Pair & Share session
 

To open the file of the mind map download Xmind that I used during the webinar (there is a free version of the software).

Feel free to improve the mind map with your own ideas.

Methodology of the "Pair & Share" exercise

Please find below a few slides about the "Pair & Share" exercise (methodology and advantages).

 
Download the presentation of the Pair Share exercise

Related posts on the SimTrade blog

   ▶ Prof. François LONGIN Sur les traces de Wilhelm von Humboldt

Useful resources

SimTrade Demo certificate

SimTrade Courses

SimTrade Simulations

Harvard Business School Global Colloquium on Participant-Centered Learning

About the author

The article was written in December 2024 by Professor François LONGIN (ESSEC Business School, Finance Department).

Understanding the Order Book: How It Impacts Trading

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Understanding the Order Book: How It Impacts Trading

Federico De ROSSI

In this article, Federico DE ROSSI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2023) talks about the order book and explains its role in financial markets.

Introduction

Understanding the order book is critical when it comes to trading in financial markets. In this article, we’ll go over what an order book is and how it affects trading.

What is an order book?

An order book for a stock, currency, or cryptocurrency is a list of buy and sell limit orders for that asset. It shows the pricing at which buyers and sellers are willing to negotiate, as well as the total number of orders available at each price. The order book is a necessary component of every trading platform since it gives a snapshot of the current market situation, of the price of the assets, and of the liquidity of the market. Thus, it is a crucial tool for traders who want to make informed decisions when entering or exiting deals.

How does an order book work?

The order book is a constantly updated record of buy and sell orders. When a trader puts a limit order, it is placed in the order book at the stated price. As a result, there is a two-sided market with distinct prices for buyers and sellers.

The order book is divided into two sections: bid (buy) and ask (sell). All open buy orders are displayed on the bid side, while all open sell orders are displayed on the ask side. The order book also shows the total volume of buy and sell orders at each price level.

In Tables 1 and 2 below, we give below two examples of order book from online brokers. We can see the two parts of the order book side by side: the “Buy” part and the “Sell” part. Every line of the order book corresponds to a buy or sell proposition for a give price (“Buy” or “Sell” columns) and a given quantity (“Volume” columns). For a given line there may be one or more orders for the same price. When there are several orders, the quantity in the “Volume” column is equal to the sum of the quantities of the different orders. Associated to the order book, there is often a chart which indicates the cumulative quantity of the orders in the order book at a given price. This chart gives an indication of the liquidity of the market in terms of market spread, market breadth, and market depth (see below for more explanations about theses concepts).

The “Buy” and “Sell” parts of the order book can be presented side by side (Table 1) or above each other (Tables 2 and 3) with the “Sell” part (in red) above the “Buy” part (in green) as the price limits of the sell limit orders are always higher than the price limits of the buy limit orders.

Table 1. Example of an order book (buy and sell parts presented side by side).
Order book
Source: online broker (Fortuneo).

Table 2. Example of an order book (buy and sell parts presented above each other).
Order book
Source: online broker (Cryptowatch).

Table 3. Example of an order book (buy and sell parts presented next to each other).
Order book
Source: online broker (Binance).

In a typical order book, the buy side is organized in descending order, meaning that the highest buy orders (i.e., the orders with the highest bid prices) are listed first, followed by the lower buy orders in descending order of price. The highest buy order in the book represents the best bid price, which is the highest price that any buyer is currently willing to pay for the asset.

On the other side of the order book, the sell side is organized in ascending order, with the lowest sell orders (i.e., the orders with the lowest ask prices) listed first, followed by the higher sell orders in ascending order of price. The lowest sell order in the book represents the best ask price, which is the lowest price that any seller is currently willing to accept for the asset.

This organization of the order book makes it easy for traders to see the current market depth and the best available bid and ask prices for an asset. When a buy order is executed at the best ask price or a sell order is executed at the best bid price, the order book is updated in real-time to reflect the new market depth and the new best bid and ask prices.

Table 4 below represents how the order book (limit order book) in trading simulations the SimTrade application.

Table 4. Order book in the SimTrade application.
Order book in the SimTrade application

You can understand how the order book works by launching a trading simulation on the SimTrade application.

The role of the order book in trading

As mentioned before, the order book is incredibly significant in trading. It acts as a market barometer, delivering real-time information about the supply and demand for an asset. Traders can also use the order book to determine market sentiment. If the bid side of the order book is strongly occupied, for example, it could imply that traders are optimistic on the asset. Thanks to the data in the order book, traders can get different information out of it.

Three characteristics of the order book

Market spread

The market spread, also known as the bid-ask spread, is the difference between the highest price a buyer is willing to pay for an asset (the bid price) and the lowest price a seller is willing to accept (the ask price) at a particular point in time.

The market spread is a reflection of the supply and demand for the asset in the market, and it represents the transaction cost of buying or selling the asset. In general, a narrow or tight spread indicates a liquid market with a high level of trading activity and a small transaction cost, while a wider spread suggests a less liquid market with lower trading activity and a higher transaction cost.

Market breadth

Market breadth is a measure of the overall health or direction of a market, sector, or index. It refers to the number of individual stocks that are participating in a market’s movement or trend, and can provide insight into the underlying strength or weakness of the market.

Market breadth is typically measured by comparing the number of advancing stocks (stocks that have increased in price) to the number of declining stocks (stocks that have decreased in price) over a given time period. This ratio is often expressed as a percentage or a ratio, with a higher percentage or ratio indicating a stronger market breadth and a lower percentage or ratio indicating weaker breadth.

For example, if there are 1,000 stocks in an index and 800 of them are increasing in price while 200 are decreasing, the market breadth ratio would be 4:1 or 80%. This would suggest that the market is broadly advancing, with a high number of stocks participating in the upward trend.

Market depth

Finally, market depth is a measure of the supply and demand of a security or financial instrument at different prices. It refers to the quantity of buy and sell orders that exist at different price levels in the market. Market depth is typically displayed in a market depth chart or order book.

It can provide valuable information to traders and investors about the current state of the market. A deep market with large quantities of buy and sell orders at various price levels can indicate a liquid market where trades can be executed quickly and with minimal impact on the market price. On the other hand, a shallow market with few orders at different price levels can indicate a less liquid market where trades may be more difficult to execute without significantly affecting the market price.

Analyzing order book data

Data from order books can be used to gain insight into market sentiment and trading opportunities. For example, traders can use the bid-ask spread to determine an asset’s liquidity. They can also examine the depth of the order book to determine the level of buying and selling interest in the asset. Traders can also use order book data to identify potential trading signals. For example, if the bid side of the order book is heavily populated at a certain price level, this could indicate that the asset’s price is likely to rise. On the other hand, if the ask side is heavily populated at a certain price level, it could indicate that the asset’s price is likely to fall.

Benefits of using order book data for trading

Using order book data can provide traders with a number of advantages.

For starters, it can be used to gauge market sentiment and identify potential trading opportunities.

Second, it can assist traders in more effectively managing risk. Traders can identify areas of support and resistance in order book data, which can then be used to set stop losses and take profits.

Finally, it can aid traders in the identification of potential trading signals. Traders can identify areas of potential buying and selling pressure in order book data, which can then be used to enter and exit trades.

How to use order book data for trading

Traders can use order book data to gain a competitive advantage in the markets. To accomplish this, they must first identify areas of support and resistance that can be used to set stop losses and profit targets.

Traders should also look for indications of buying and selling pressure in the order book. If the bid side of the order book is heavily populated at a certain price level, it could indicate that the asset’s price is likely to rise. On the other hand, if the ask side is heavily populated at a certain price level, it could indicate that the asset’s price is likely to fall.

Finally, traders should use trading software to automate their strategies. Trading bots can be set up to monitor order book data and execute trades based on it. This allows traders to capitalize on trading opportunities more quickly and efficiently.

Conclusion

To summarize, the order book is a vital instrument for financial market traders. It gives real-time information about an asset’s supply and demand, which can be used to gauge market mood and find potential trading opportunities. Traders can also utilize order book data to create stop losses and take profits and to automate their trading techniques. Traders might obtain an advantage in the markets by utilizing the power of the order book.

Related posts on the SimTrade blog

▶ Jayna MELWANI The impact of market orders on market liquidity

▶ Lokendra RATHORE Good-til-Cancelled (GTC) order and Immediate-or-Cancel (IOC) order

▶ Clara PINTO High-frequency trading and limit orders

▶ Akshit GUPTA Analysis of The Hummingbird Project movie

Useful resources

SimTrade course Trade orders

SimTrade course Market making

SimTrade simulations Market orders   Limit orders

About the author

The article was written in March 2023 by Federico DE ROSSI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2023).

Hedging of the crude oil price

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Youssef_Louraoui

In this article, Youssef Louraoui (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) discusses the concept of hedging and its application in the crude oil market.

This article is structured as follow: we introduce the concept of hedging in the first place. Then, we present the mathematical foundation of the Minimum Variance Hedging Ratio (MVHR). We wrap up with an empirical analysis applied to the crude oil market with a conclusion.

Introduction

Hedging is a strategy that considers taking both positions in the physical as well as the futures market to offset market movement and lock-in the price. When an individual or a corporation decides to hedge risk using futures markets, the objective is to take the opposite position to neutralize the risk as far as possible. If the company is long on the physical side (say a producer), they will mitigate the hedging by taking a short exposure in the future market. The opposite is true for a market player who is short physical. He will seek to have a long exposure in the futures market to offset the risk (Hull, 2006).

Short hedge

Selling futures contracts as insurance against an expected decrease in spot prices is known as a short hedge. For instance, an oil producer might sell crude futures or forwards if they anticipate a decline in the price of the commodity.

Long hedge

A long hedge involves purchasing futures as insurance against an increase in price. For instance, an aluminum smelter will purchase electricity futures and forward contracts, allowing the business to secure its electricity needs in the event that the physical market rises in value.

Mathematical foundations

Linear regression model

We can consider the hedge ratio as the slope of the following linear regression representing the relationship between the spot and futures price changes:

doc_SimTrade_MVHR_formula_4

where

  • ∆St the change in the spot price at time t
  • β represents the hedging parameter
  • ∆Ft the change in the futures price at time t

The linear regression model above can also be expressed with returns instead of price changes:

doc_SimTrade_MVHR_formula_5

  • RSpot the return in the spot market at time t
  • RFutures the return in the futures market at time t

Hedge ratio

We can derive the following formula for the Minimum Variance Hedging Ratio (MVHR) denoted by the Greek letter beta β:

doc_SimTrade_MVHR_formula_3

where

  • Cov(∆St,∆Ft) the co movement of the change in spot price and futures price at time t
  • Var(∆Ft) represents the variance of the change in price of the future price at time t

The variance and covariance of spot and futures prices are time-varying due to the changing distributional features of these values across time. Accordingly, taking into consideration such dynamics in the variance and covariance term of asset prices is a more acceptable method of establishing the minimal variance hedge ratio. There is a number of different methods that account for the dynamic nature of the minimal variance hedge ratio estimation (Alizadeh, 2022):

  • Simple Rolling OLS
  • Rolling VAR or VECM
  • GARCH models
  • Markov Regime Switching
  • Minimising VaR and CVaR methods

Empirical approach to hedging analysis

Periods

We downloaded ten-year worth of weekly data for the WTI crude oil spot and futures contract from the US Energy Information Administration (EIA) website. We decompose the data into two periods to assess the effectiveness of the different hedging strategies: 1st period from 23rd March 2012 to 24th March 2017 and 2nd period from 31st March 2017 to 22nd March 2022.

First period: March 2012 – March 2017

The first five years are used to estimate the Minimum Variance Hedging Ratio (Ederington, 1979). We can approach this question by using the “=slope(known_ys, known_xs)” function in Excel to obtain the gamma coefficient that would represent the MVHR. When computing the slope for the first period of the sample from 23rd March 2012 to 24th March 2017, we get a MVHR equal to 0.985. We obtain a correlation (ρ) using the Excel formula “=correl(array_1, array_2)” highlighting the logarithmic return of WTI spot and futures contract price, which yields 0.986. We can see from the figure 1 how the spot and futures prices converge closely and track each other in a very tight corridor, with very minor divergence. The regression plot between spot and futures contract returns for the first period is shown in Figure 2. This suggests that the hedger should take an opposite position in the futures market equal to 0.985 contract for each spot contract in order to minimise risk when using futures contracts as a hedging tool.

Figure 1. WTI spot and futures (1 month) prices
March 2012 – March 2017
WTI spot and futures prices
Source: computation by the author (data: EIA & Refinitiv Eikon).

Figure 2. Linear regression of WTI spot return on futures (1 month) return
March 2012 – March 2017
Linear regression of WTI spot return on futures (1 month) return
Source: computation by the author (data: EIA & Refinitiv Eikon).

A one-to-one hedge ratio (also known as naïve hedge) means that for every dollar of exposure in the physical market, we take one dollar exposure in the futures market. The effectiveness of this strategy is tied closely to how the spot/futures market correlation behaves. The effectiveness of this strategy would be equal to the correlation of the spot and the futures market prices in the second period.

Second period: March 2017 – March 2022

We compute the MVHR for the second period with the same approach retained in the first part by using the “=slope(known_ys, known_xs)” function in Excel to obtain the gamma coefficient that would represent the MVHR. When computing the slope for the first period of the sample from 23rd March 2017 to 24th March 2022, we get a MVHR equal to 1.095. This means that for every spot contract that we own, we need to buy 0.985 futures contracts to hedge our market risk. As previously stated, the same trend can be seen in figure 3, where spot and futures prices converge closely and track each other with just little deviation. Figure 4 represents the regression plot between spot and futures contract returns for the second period. This means that in order to reduce risk to the minimum possible amount when futures contract used as hedging instrument, for each spot contract the hedger should take an opposite position equivalent to 1.05 contract in the futures market.

Figure 3. WTI spot and futures (1 month) prices
March 2017 – March 2022.
WTI spot and futures prices
Source: computation by the author (data: EIA & Refinitiv Eikon).

Figure 4. Linear regression of WTI spot return on futures (1 month) return
March 2017 – March 2022
Linear regression of WTI spot return on futures (1 month) return
Source: computation by the author (data: EIA & Refinitiv Eikon).

We can approach this hedging exercise in a time-varying framework. Some academics consider that covariance and correlation are not static parameters, so they came up with models to accommodate for the time-varying nature of these two parameters. We can compute the rolling regression as the rolling slope by changing the timeframe to allow for dynamic coefficients. For this example, we computed rolling regression for one month, three-month, one year and two years. We can plot the rolling regression in the graph below (Figure 5). We can average the rolling gammas and obtain an average for each rolling period (Table 1):

Table 1. Table capturing the rolling hedge ratio for WTI across different horizons.
Hedging strategy
Source: computation by the author (data: EIA & Refinitiv Eikon).

Figure 5. WTI hedge ratio for different rolling window sizes.
Hedge ratio for WTI for rolling window sizes
Source: computation by the author (data: EIA & Refinitiv Eikon).

Conclusion

In an realistic setting, these results may be oversimplified. In some instances, cross hedging is required to calculate this strategy. This technique is used to hedge an asset’s value by relying on another asset to replicate its behaviour. Let’s use an airline as an example of a corporation seeking to hedge its jet fuel expenditures. As there is currently no jet fuel futures contract, the airline can hedge its basis risk with heating oil (an equivalent product with a valid futures market). As stated previously, the degree of correlation between the spot price and the futures price impacts the precision of cross-hedging (and hedging in general). To get the desired results and avoid instances in which we overhedge or underhedge our exposure, hedging must finally be performed appropriately.

You can find below the Excel spreadsheet that complements the explanations about of this article.

Hedging strategy on crude oil

Why should I be interested in this post?

Understanding hedging techniques can be a valuable tool to implement to reduce the downside risk of an investment. Implementing a good hedging strategy can help professionals to better monitor and modify their trading strategies based different market environments.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI My experience as an Oil Analyst at an oil and energy trading company

   ▶ Youssef LOURAOUI Introduction to Hedge Funds

   ▶ Youssef LOURAOUI Global macro strategy

   ▶ Youssef LOURAOUI Minimum volatility factor

   ▶ Youssef LOURAOUI VIX index

   ▶ Jayati WALIA Black Scholes Merton option pricing model

   ▶ Jayati WALIA Implied volatility

   ▶ Youssef LOURAOUI Portfolio

Useful resources

Academic research

Adler M. and B. Dumas (1984) “Exposure to Currency Risk: Definition and Measurement” Financial Management 13(2) 41-50.

Alizadeh A. (2022) Volatility of energy prices: Estimation and modelling. Oil and Energy Trading module at Bayes Business School. 46-51.

Ederington L.H. (1979). The Hedging Performance of the New Futures Markets. Journal of Finance, 34(1) 157-170.

Hull C.J. (2006). Options, futures and Other Derivatives, sixth edition. Pearson Prentice Hall. 99-373.

Business

US Energy Information Administration (EIA)

About the author

The article was written in January 2023 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Market efficiency: the case study of Yes bank in India

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Market efficiency: the case study of Yes bank in India

Aamey MEHTA

In this article, Aamey MEHTA (ESSEC Business School, Master in Finance, Singapore campus, 2022-2023) explains the key financial concept of market efficiency.

What is Market Efficiency?

An informationally efficient market is a market in which the current price of a security fully, rationally, and quickly reflects all information of that security

We can measure the efficiency of a market by observing the lag between the time that information is received to the time that the security’s price reflects this information. If there is a large lag, then traders can make use of this information to generate positive returns. For efficient markets the price of a security should not be affected by information that is already expected. The changes in price should be due to new information, i.e., information that was unexpected. For example: if a company’s earning is expected to be $10M (market consensus) and their earnings are $10M, this should not cause a change in the company’s price. However, if the earnings were $20M or $5M, then the shock news will cause the stock price to move upwards or downwards.

Market efficiency and investment styles

In a perfectly efficient market investors should use a passive investment strategy. This is because in such a market it is not possible to beat the market. In efficient markets investors can expect the market value of an assets to be equal to its intrinsic value. Using an active strategy will result in underperformance compared to the market due to transaction costs. However, if the market is inefficient, then active investment strategies can result in a profit for the investor.

What factors affect market efficiency?

Generally, markets are neither perfectly efficient or inefficient. The degree of efficiency depends on the following factors: the number of market participants, the availability of Information, and impediments to trading.

Number of market participants

The higher the number of market participants the more efficient the market is. Market participants include investors, traders, analysts, and people who follow the market. The number of participants can vary over time and across countries. Some countries prevent foreigners from trading on their markets which reduces market efficiency.

Availability of Information

The more information that is available to the investors, more efficient the market is. The easier and cheaper it is to access the information the more efficient the market will be. The access to information should not favor one group over another and should be equally available to all participants. If participants have access to material nonpublic information about the firm they should not trade on this information as this would constitute insider trading which is illegal. In developed markets there is abundance of information, and the markets are efficient. Example: New York Stock Exchange. In less developed markets the availability of information is lower and hence markets are less efficient. Example: the forwards market.

Impediments to trading

Arbitrage refers to buying an asset in one market and simultaneously selling it in another market at a higher price. This buying and selling will continue till price in both the markets are the same and arbitrage is no longer possible. Impediments to trading such as high transaction costs will restrict arbitrage opportunities and allow for some mispricing of assets.

Short selling prevents assets from being overvalued and hence short selling improves market efficiency. Restrictions on short selling, such as inability to borrow stock cheaply will reduce efficiency.

Transaction and information costs

If the cost of gathering information, analysis and trading is more than the cost of trading misvalued assets markets will be inefficient. If after deducting costs, there is no risk adjusted returns to be made from trading based on publicly available information then the markets are said to be efficient.

Types of market efficiency

Weak form of market efficiency

This form of market efficiency states that current security prices fully reflect all currently available security market data. Thus, past price and volume information will have no predictive power over the future direction of security prices because price changes will be independent from one period to the next.

Semi-strong form of market efficiency

This form holds that security prices rapidly adjust without bias to the arrival of new public information. Current security prices fully reflect all publicly available information. This form says that security prices include all past security market and non-market information available to the public. Examples: Information on the financials reports published by the company, news about the company.

Strong form of market efficiency

This form states that security prices fully reflect all information from both public and private sources. The strong form includes all types of information: past security market information, public and private (insider) information. This means that no group of investors has monopolistic access to information relevant to the formation of prices and no one should be able to generate positive risk adjusted returns.

What do we know about the efficiency of the market?

Fama

Fama, in his paper Efficient Market Hypothesis defined a market to be “informationally efficient” if prices at each moment incorporate all available information about future values.

The efficient market hypothesis states:

  • Current prices incorporate all available information and expectations.
  • Current prices are the best approximation of intrinsic value.
  • Price changes are due to unforeseen events.
  • “Mispricings” do occur but not in predictable patterns that can lead to consistent outperformance.

The efficient market hypothesis does not state:

  • All investors are rational.
  • Prices are always right.
  • Prices should be stable.
  • Professional money managers can’t earn higher than market returns.

The Grossman-Stiglitz paradox

This paradox was proposed by Stanford Grossman and Joseph Stiglitz. They argue that perfectly informationally efficient markets are an impossibility since, if prices perfectly reflected available information, there is no profit to gathering information, in which case there would be little reason to trade and markets would eventually collapse.

Investors that purchase index funds or ETFs benefit at the expense of investors who pay for financial services either indirectly or directly via investing in actively managed funds.

Case study: yes bank

Yes Bank is an Indian Bank founded in 2004 by Rana Kapoor and Ashok Kapur, headquartered in Mumbai, India.

Yes bank is a private sector bank. In March 2020, Yes Bank faced a historical crisis. There are various reasons that led Yes bank to this crisis, they are, there were a large number of bad loans given by banks and depositors have withdrawn large numbers of amounts from the bank. There was no balance between the loan sheet and the depositors’ sheet. RBI put a 30 days moratorium on Yes Bank to save it.
A major effect of the yes bank crisis was that there was a big chance that other financial institutions could collapse. But the Reserve Bank of India took initiative and saved Yes Bank from major collapse.

In May 2020 shares of Yes Bank Ltd. fell as much as 84.65 percent intraday to Rs 5.65 apiece—the lowest on record—but pared some of the losses to traded 51.63 percent lower at Rs 17.80. The S&P BSE Sensex fell 1,450 points and NSE Nifty 50 slipped below 10,900. This, after the Reserve Bank of India on Thursday evening superseded the board of the lender and imposed curbs on its operations for a month.

stock chart of yes bank
Logo of Wells Fargo
Source: internet.

Useful resources

Academic resources

Fama E. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25,383-417.

Fama E. (1991) Efficient Capital Markets: II Journal of Finance, 46, 1575-617.

Grossman S.J. and J.E. Stiglitz (1980) On the Impossibility of Informationally Efficient Markets The American Economic Review, 70, 393-408.

Business resources

Yes bank

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Passive Investing

   ▶ Youssef LOURAOUI Active Investing

   ▶ Akshit GUPTA Portfolio manager – Job description

About the author

The article was written in November 2022 by Aamey MEHTA (ESSEC Business School, Master in Finance, Singapore campus, 2022-2023)

Reverse Convertibles

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Reverse Convertibles

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains reverse convertibles, which are a structured product with a fixed-rate coupon and downside risk.

Introduction

The financial market has been ever evolving, witnessing the birth and flourish of novel financial instruments to cater to the diverse needs of market participants. On top of plain vanilla derivative products, there are exotic ones (e.g., barrier options, the simplest and most traded exotic derivative product). Even more complex, there are structured products, which are essentially the combination of vanilla or exotic equity instruments and fixed income instruments.

Amongst the structured products, reverse convertible products are one of the most popular choices for investors. Reverse convertible products are non-principal protected products linked to the performance of an underlying asset, usually an individual stock or an index, or a basket of them. Clients can enter into a position of a reverse convertible with the over-the-counter (OTC) trading desks in major investment banks.

In exchange for an above-market coupon payment, the holder of the product gives up the potential upside exposure to the underlying asset. The exposure to the downside risks still remains. Reserve convertibles are therefore appreciated by the investors who are anticipating a stagnation or a slightly upward market trend.

Construction of a reverse convertible

This product could be decomposed in two parts:

  • On the one hand, the buyer of the structure receives coupons on the principal invested and this could be considered as a “coupon bond”;
  • On the other hand, the investor is still exposed to the downside risks of the underlying asset and foregoes the upside gains, and this could be achieved by a short position of a put option (either a vanilla put option or a down-and-in barrier put option).

Positions of the parties of the transaction

A reverse convertible involves two parties in the transaction: a market maker (investment bank) and an investor (client). Table 1 below describes the positions of the two parties at different time of the life cycle of the product.

Table 1. Positions of the parties of a reverse convertible transaction

t Market Maker (Investment Bank) Investor (Client)
Beginning
  • Enters into a long position of a put (either a vanilla put or a down-and-in barrier put)
  • Receives the nominal amount for the “coupon” part
  • Invests in the amount (nominal amount plus the premium of the put) in risk-free instruments
  • Enters into a short position of a put (either a vanilla put or a down-and-in barrier put)
  • Pays the nominal amount for the “coupon” part
Interim
  • Pays pre-specified interim coupons in respective interim coupon payment dates (if any)
  • Receives interest payment from risk-free investments
  • Receives the pre-specified interim coupons in respective interim coupon payment dates (if any)
End
  • Receives the payoff (if any) of the put option component
  • Pays the pre-specified final coupon in the final coupon payment date
  • Pays the payoff (if any) of the put option component
  • Receives the pre-specified final coupon in the final coupon payment date

Based on the type of the put option incorporated in the product (either plain vanilla put option or down-and-in barrier put option), reserve convertibles could be categorized as plain or barrier reverse convertibles. Given the difference in terms of the composition of the structured product, the payoff and pricing mechanisms diverge as well.

Here is an example of a plain reverse convertible with following product characteristics and market information.

Product characteristics:

  • Investment amount: USD 1,000,000.00
  • Underlying asset: S&P 500 index (Bloomberg Code: SPX Index)
  • Investment period: from August 12, 2022 to November 12, 2022 (3 months)
  • Coupon rate: 2.50% (quarterly)
  • Strike level : 100.00% of the initial level

Market data:

  • Current risk-free rate: 2.00% (annualized)
  • Volatility of the S&P 500 index: 13.00% (annualized)

Payoff of a plain reverse convertible

As is presented above, a reverse convertible is essentially a combination of a short position of a put option and a long position of a coupon bond. In case of the plain reverse convertible product with the aforementioned characteristics, we have the blow payoff structure:

  • in case of a rise of the S&P 500 index during the investment period, the return for the reverse convertible remains at 2.50% (the coupon rate);
  • in case of a drop of the S&P 500 index during the investment period, the return would be equal to 2.50% minus the percentage drop of the underlying asset and it could be negative if the percentage drop is greater than 2.5%.

Figure 1. The payoff of a plain reverse convertible on the S&P 500 index
Payoff of a plain reverse convertible
Source: Computation by author.

Pricing of a plain reverse convertible

Since a reverse convertible is essentially a structured product composed of a put option and a coupon bond, the pricing of this product could also be decomposed into these two parts. In terms of the pricing a vanilla option, the Black–Scholes–Merton model could do the trick (see Black-Scholes-Merton option pricing model) and in terms of pricing a barrier option, two methods, analytical formula method and Monte-Carlo simulation method, could be of help (see Pricing barrier options with analytical formulas; Pricing barrier options with simulations and sensitivity analysis with Greeks).

With the given parameters, we can calculate, as follows, the margin for the bank with respect to this product. The calculated margin could be considered as the theoretical price of this product.

Table 2. Margin for the bank for the plain reverse convertible
Margin for the bank for the plain reverse convertible
Source: Computation by author.

Download the Excel file to analyze reverse convertibles

You can find below an Excel file to analyze reverse convertibles.
Download Excel file to analyze reverse convertibles

Why should I be interested in this post

As one of the most traded structured products, reverse convertibles have been an important instrument used to secure return amid mildly negative market prospect. It is, therefore, helpful to understand the product elements, such as the construction and the payoff of the product and the targeted clients. This could act as a steppingstone to financial product engineering and risk management.

Related posts on the SimTrade blog

   ▶ All posts about options

   ▶ Jayati WALIA Black-Scholes-Merton option pricing model

   ▶ Akshit GUPTA The Black Scholes Merton Model

   ▶ Shengyu ZHENG Barrier options

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

   ▶ Shengyu ZHENG Pricing barrier options with simulations and sensitivity analysis with Greeks

Resources

Academic references

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

De Bellefroid, M. (2017) Chapter 13 (Barrier) Reverse Convertibles. The Derivatives Academy. Accessible at https://bookdown.org/maxime_debellefroid/MyBook/barrier-reverse-convertibles.html

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

Merton, R. (1973). Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E. S. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D.R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7, 267-311.

Business references

Six Structured Products. (2022). Reverse Convertibles et barrier reverse Convertibles

About the author

The article was written in August 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).