Financial securities are becoming increasingly complex these days owing to change in structure, valuation and risk elements. Pricing these securities is quite a tricky work, hence there is a lot of demand for professionals who can work on them. These professionals are expected to build mathematical models such that the assets trade at an optimal price, generate profit and has a lower risk element. This branch of study is known as Quantitative Finance or popularly Quant Finance.
Why is there a rise in quant finance?
This is an era of data deluge. The availability of vast amount of data or information casts an impact on asset pricing. Everyone is looking at the optimal pricing of securities to earn maximum gains. Research has shown that a combination of statistics, mathematics, analytics and programming can do wonders in this field. High-end open source software and leveraged cloud computing have made it easier for many smaller firms to foray into this field.
After the financial crisis of 2008, the regulatory environment for the trading of securities has become much more stringent. The requirement for risk transparency is like never before. The risk management industry globally has pulled up their socks and there is an increased requirement of assets with lower risk quotient.
Algorithmic funds and a rise of automated trading are also reasons that have led to increased demand for Quant Finance.
What are the models used in Quant Finance?
As mentioned above, quantitative finance uses many mathematical as well as statistical models to derive the value of an asset. Few of the commonly used models are:
- Portfolio optimization model
- Derivatives pricing model based on Black Scholes
- Stochastic Calculus-based models
- Algorithmic model for Fraud detection
What are the various applications of Quant Finance?
Quant Finance is mainly used in asset-pricing as well as risk mitigation. However, there are many areas in the financial world wherein Quant Finance is used as a crucial tool. Some of these areas are:
A diversified portfolio includes stocks, bonds, derivatives and commodities. A Fund Manager is constantly at work to hedge the risk, track the assets and outperform the benchmark. To achieve this, he needs to study the behaviour and relation of the various asset classes with each other. He needs to assess the pattern of price movement in relation to the other. With so much data available, statistical models, analytics and Machine Learning can help him analyse the movement of asset prices and maximise gains.
There is a lot of speculation that goes on in the currency markets. Traders bet on currencies, bond yields and movement of dollars. So much speculation leads to volatility in the currency markets. Use of Quant Finance helps to estimate and hedge against exchange rate risk and bond default risk. “What amount to the asset should be bought at what price?” is a classic question that Quant Finance helps to find out.
Trading platforms have become automated today. Hence, there is a lot of requirement for algorithmic model creation. The skills related to handling these technical platforms range from mathematical modelling, statistics and coding.
After the financial crisis of 2008 and the Eurozone crisis that followed it shortly, global traders and investors have become increasingly cautious. Everyone wants to know precisely the quantum of credit and market risk to be borne. Not just traders, even insurance companies need to do a lot of computations for the asset-liability matching for which there is high demand for quants.
What kind of skills do you need for Quant Finance?
Quants being a blend of many disciplines requires diverse skill sets. Let us have a look at the must-have skills for a Quant Finance professional:
(1) Mathematics and Statistics: High-level understanding of the Applied Mathematics and Statistics in the field of finance and Economics is a must. Probability, Stochastic Calculus, Time Series analysis and Combinatorics are some of the areas where expertise is expected. Thus, it is preferable to have formal qualifications like PhD or Masters in Mathematical Finance, Econometrics, Financial Engineering or M.Sc in statistics.
(2) Programming: A fair knowledge of Database Management and programming languages such as Python, C++, R, SQL, .NET and VBA
(3) Statistical Software: Hands-on knowledge of statistical software such as Matlab, S-Plus, SAS and advanced Excel. Proficiency in Financial Modelling is also beneficial in Quant Finance.
(4) Data Analytics: Quant Finance professionals have to work a lot of data and derive insights from it. Hence, certification and experience in Data Analytics, Data Science or Big Data is of great advantage.
(5) Machine-Learning: Machine Learning has huge potential in Quant Finance. Due to a democratization of information, many firms want to change the ecosystem. Today analysts are being replaced and big companies are choosing to rely on Artificial Intelligence and Machine Learning algorithms.
Quant Finance is the backbone of a strong economy
A strong financial system is a foundation of a resilient economy, and this can be achieved only when calculated moves are made at each juncture. Quantitative Finance professionals have the power to transform raw data into intelligent business decisions through statistical models and data analytics. This is only possible when you have a strong educational background in Mathematics, Statistics and Computer Programming. Other skills can surely be picked up in due course. It goes without saying that such professionals with high-level technical skills earn lucrative remuneration as well. As per Payscale.com, the average salary of a Quant Finance Analyst in India is more than Rs 10 lacs per annum. With a dynamic domestic and global economy, political headwinds and changing the regulatory landscape, Quant Finance is here to stay and all set to boom in the future.