A “quant” is a quantitative financial analyst, also known as a computational financial analyst or a financial engineer. Unlike traditional analysts, quants rely on a heavy amount of mathematics to spot investment opportunities, such as arbitrage in the derivatives market.
A derivative is a financial instrument tied to an underlying asset, an example being stock options that give the holder the right (though not the obligation) to trade a company’s share at a specified price regardless of the market value. Because the option caries a right, economically it must have a price. This price is determined via a model known as Black-Scholes, which is presented in the form of a stochastic differential equation. To trade effectively at a price, the quant must determine a numerical approximation to the equation, possibly via Monte Carlo simulations or finite difference methods.
Notice that many of these terms are common to other computational sciences, such as fluid dynamics. For this reason, many quants rely on similar tools that physicists and statisticians employ. Thus, many codes that needed absolute execution performance were written in C++. The open source QuantLib library may be used here, and the commercial SciFinance has the special ability to generate C++ code based on a higher-level language. Indeed, given the recent trends in programming tools for high-performance technical computing, many users may instead develop pricing systems in MATLAB, Mathematica, Visual Basic, and Excel.
Of note, the next version of Excel will be able to execute computations in parallel on Windows CCS. It may be that in the future, quants will receive real-time results with a finance-specific set of development tools. It seems that now is a good time to brush-up on the Ito Lemma.