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Quantitative analyst

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A quantitative analyst is a person who works in the financial markets developing and implementing mathematical models to assist the activities of traders and risk managers within investment banks, hedge funds and other financial institutions. Throughout the industry, such professionals are known as quants. The disciplines of finance, mathematics, statistics and computer science are now linked. A corporation whose risk management policy compels it to lock in a foreign exchange rate must deal with a foreign currency derivatives trader. The trader bases his pricing and hedging decisions on the behavior of a Brownian motion, determined by statistical estimation of parameters and simulated under probabilities that differ from those of the real world, a simulation justified by the profound mathematical and financial dual ideas of change-of-measure and risk-neutral pricing. Although the original "quants" were concerned with risk management and derivatives pricing, the meaning of the term has expanded over time to include those individuals involved in almost any application of mathematics in finance. An example is statistical arbitrage.

1 Education
2 History
3 Famous quants
4 See also
5 References

Education

Quants often come from physics, engineering or mathematics backgrounds rather than finance related fields, and quants are a major source of employment for people with physics, mathematics, and engineering Ph.D's. Typically a quant will also need extensive skills in computer programming. This demand for quants has led to the creation of specialized Masters and PhD courses in mathematical finance, computational finance, and/or financial reinsurance. In particular, Masters degrees in financial engineering and financial analysis are becoming more popular with students and with employers. Carnegie Mellon's Tepper School of Business, which created the Masters degree in financial engineering, reported a 21% increase in applicants to their MS in Computational Finance program, which is on top of a 48% increase in the year before^[1]. The University of California Berkeley's program in Financial Engineering through their Haas School of Business admits 60 students each year. Other well known programs are provided by the University of Chicago, Cornell University, Indiana University, Columbia University, Purdue University, and New York University. This surge in popularity has led other schools (the University of California at Los Angeles, Rutgers University, Polytechnic University, the University of Michigan, the University of Minnesota) and the Nanyang Technological University (Singapore) to add Masters level degrees. These Masters level programs are generally one year in length and more focused than the broader MBA degree.

History

Quantitative finance started in the U.S. in the 1930s as some astute investors began using mathematical formulas to price stocks and bonds. Harry Markowitz's 1952 Ph.D thesis "Portfolio Selection" was one of the first papers to formally adapt mathematical concepts to finance. Markowitz formalized a notion of mean return and covariances for common stocks which allowed him to quantify the concept of "diversification" in a market. He showed how to compute the mean return and variance for a given portfolio and argued that investors should hold only those portfolios whose variance is minimal among all portfolios with a given mean return. Although the language of finance now involves Ito calculus, minimization of risk in a quantifiable manner underlies much of the modern theory. In 1969 Robert Merton introduced stochastic calculus into the study of finance. Merton was motivated by the desire to understand how prices are set in financial markets, which is the classical economics question of "equilibrium," and in later papers he used the machinery of stochastic calculus to begin investigation of this issue. At the same time as Merton's work and with Merton's assistance, Fischer Black and Myron Scholes were developing their option pricing formula, which led to winning the 1997 Nobel Prize in Economics. It provided a solution for a practical problem, that of finding a fair price for a European call option, i.e., the right to buy one share of a given stock at a specified price and time. Such options are frequently purchased by investors as a risk-hedging device. In 1981, Harrison and Pliska used the general theory of continuous-time stochastic processes to put the Black-Scholes option pricing formula on a solid theoretical basis, and as a result, showed how to price numerous other "derivative" securities.