Following the financial crisis of 2008, regulators implemented a number of safeguards to reduce the probability of a recurrence. Among these were more stringent requirements for banks and other financial institutions to pass periodic stress tests: simulations designed to determine the institutions’ capacity to survive various adverse economic scenarios. Stress testing requires a high level of expertise, as the process involves the application of numerous complex mathematical formulae to massive amounts of data. It’s the sort of expertise one gains through the study of quantitative finance.
What is quantitative finance?
Quantitative finance—which employs data mining and advanced mathematics to analyze markets and calculate the probabilities of potential outcomes—is a cousin of financial engineering. Both fields draw from advanced mathematics, computer science, and business to analyze financial markets. Their chief difference is in the mix of disciplines. Quantitative finance is rooted primarily in applied mathematics; experts in the field are often referred to as ‘quants.’
An illustrative example helps clarify the distinction. Suppose a bank wanted to project both potential return and degree of risk for its options holdings. The bank’s quantitative finance team would determine which mathematical applications best address these questions and would then develop models to project solutions. It would hand its work off to the financial engineering team, which would take those models and use them to build tools that turn the solutions into practice. In their respective work, the quantitative finance team would likely focus more on what is theoretically possible in pursuit of maximizing return and reducing risk, while the financial engineering team would more likely factor in the practicability of implementation and the applicability of prevailing finance theory to the mathematical solutions.
Quantitative finance relies on the fundamental theorem of arbitrage-free pricing, which assumes some element of risk in every transaction. One of the field’s signal achievements is the Black-Scholes formula, which calculates the theoretical price of European-style options based on real-time and projected inputs.
What is a master’s in quantitative finance?
A Master of Science in Quantitative Finance (MSQF or MScQF) prepares students for careers in the quantitative measurement and analysis of financial markets. The degree is sometimes offered as a Master of Science in Mathematical Finance (MSMF or MScMF) or a Master of Science in Financial Mathematics (MSFM or MScFM). It may be offered through a university’s school of business or through its mathematics department.
Quantitative finance is commonly used in risk management and derivatives pricing, and accordingly master’s programs focus on these two fields. Core curricula typically cover investment theory, stochastic calculus, financial markets, derivatives, econometrics, risk analysis, portfolio theory, and computer programming. Most programs offer electives, although the breadth and number of options vary.
Some programs facilitate valuable networking with industry insiders and expert consultants, and some excel at promoting hands-on practice through projects, simulations, and internships. These opportunities add value to the master’s degree and should be taken into account when choosing a program.
Who are typical candidates For a master’s in quantitative finance?
Applicants to MSQF programs are expected to have successfully completed undergraduate work in multivariable calculus, linear algebra, differential equations, and probability, In addition, successful candidates typically have experience programming in C++ or a similar language. Most majored in mathematics, physics, economics, or engineering as undergraduates. They are driven, highly analytical, and, unsurprisingly, extremely intelligent.
What can I do with a master’s in quantitative finance?
Quants often find work in such roles as derivatives analyst, risk analyst, investment analyst, quantitative analyst, portfolio engineer, quantitative researcher, modeler, trader, and securities manager. Employers include investment banks, commercial banks, accounting firms, and brokerages.
Payscale.com lists a median salary for quantitative analysts at $82,792, with an additional $9,857 in bonuses and $4,750 in profit sharing. Those numbers increase in larger markets such as New York City ($97,118; $14,000; $10,000), Boston ($100,942; $15,260; $12,000), and San Francisco ($113,406; $11,792; $1,750).
The quantum increase in data creation in the new millennium, coupled with a parallel increase in computing power, has vastly increased the finance industry’s reliance of quantitative information. The demand for experts in quantitative analysis has grown apace, creating numerous opportunities in the field of quantitative finance.
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