{"id":4958,"date":"2021-08-09T21:06:28","date_gmt":"2021-08-09T20:06:28","guid":{"rendered":"https:\/\/www.simtrade.fr\/blog_simtrade\/?p=4958"},"modified":"2021-08-09T21:06:28","modified_gmt":"2021-08-09T20:06:28","slug":"quantitative-finance","status":"publish","type":"post","link":"https:\/\/www.simtrade.fr\/blog_simtrade\/quantitative-finance\/","title":{"rendered":"Quantitative Finance: Introduction and Scope"},"content":{"rendered":"

Quantitative Finance: Introduction and Scope<\/h1>\n

\"Jayati<\/a><\/p>\n

In this article, Jayati WALIA<\/a> (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022) presents\u00a0an\u00a0overview\u00a0of Quantitative Finance.<\/p>\n

Quantitative Finance: Introduction and Scope<\/h2>\n

Quantitative finance has become an integral part of modern finance with the advent of innovative technologies, trading platforms, mathematical models, and sophisticated algorithms. In lay man terms, it is essentially the application of high-level mathematics and statistics to finance problems. Quantitative finance majorly focuses on most frequently traded securities. The very basis of it involves observation and quantitative analysis of market prices (stock prices, exchange rates, interest rates, etc.) over time, along with applying them to stochastic models and deducing results to make security pricing, trading, risk assessment, hedging and many other investment decisions. Hence, the heavy involvement of mathematics and especially stochastic calculus. However, it is not limited to that. In fact, theories and concepts from many other disciplines including physics, computer science, etc. have contributed to put together what we know as quantitative finance today.<\/p>\n

Brief History<\/h2>\n

It was in the 20th century that the foundations of Quantitative Finance were laid starting off with the \u2018Theory of Speculation\u2019 PhD thesis by the French mathematician Louis de Bachelier. Bachelier applied the concept of Brownian motion to asset price behavior for the first time. Later the Japanese mathematician Kiyoshi \u00ceto wrote a paper on stochastic differential equations and founded the stochastic calculus theory that is also named after him (\u00ceto calculus) and is widely used in option pricing. The major breakthrough however, came in the 1970s when Robert Merton\u2019s \u2018On the pricing of corporate debt: the risk structure of interest rates\u2019 and Fischer Black and Myron Scholes\u2019 \u2018The pricing of options and corporate liabilities\u2019 research papers were published which inherently presented a call and put option pricing model and after that there was no looking back. The Black-Sholes-Merton model known as \u201cBSM\u201d model is widely used and is creditable for the boom of the options market. Today many more stochastic models have been devised to extend the BSM model, setting the benchmarks of quantitative analysis higher and benefitting the global economy.<\/p>\n

Market participants<\/h2>\n

Quantitative Finance is used by many market participants: banks, financial institutions, investors and businesses who want better and automated control over their finances given the fluctuating behavior of the assets they trade. Initially, quantitative finance was majorly used in modelling market finance problems like pricing and managing derivative products for trading, managing risk of the investments in contracts, etc. basically in the sell-side of the firms such as Investment Banking. However, with continuous advancements, we see increased usage in buy-side as well among areas like Hedge Funds and Asset Management through development of quantitative models to analyze asset behavior and predict market movements in order to leverage potential trading opportunities.<\/p>\n

Thus, any firm or investor that deals in financial derivatives (futures and options), portfolios of stocks and\/or bonds, etc. need to use Quantitative Finance. These participants have specialized analysts to work on the quantitative finance and they are generally known as Quantitative Analysts or \u2018quants\u2019. Once referred to as \u2018the rocket scientists of Wall Street\u2019, quants have sound understanding of finance, mathematics and statistics combined with the acumen of programming\/coding. With the dramatic changes in industry witnessed over the past years, quants with a stellar combination of the mentioned disciplines are greatly in demand.<\/p>\n

Types of Quants<\/h2>\n

Quants create and apply financial models for derivative pricing, market prediction and risk mitigation. There are however many variations in quant roles, some of which are explained below:<\/p>\n