Quants in fixed income, equities, derivatives, and commodities groups use MATLAB to prototype and implement pricing and valuation models for options, derivatives, and portfolios. They depend on the numerical accuracy, mathematical versatility, and programming efficiency of MATLAB to perform effective financial engineering.
Quants price options, derivatives, structured products, and other securities with MATLAB, using the following methods:
Quants also use MATLAB to connect current analytics with legacy pricing routines, such as those written in C++, and to combine new pricing routines with existing ones.
For quantitative analysts working in MATLAB, the ability to obtain relevant historical, real-time, and derived data from data providers and databases provides relevant input to pricing models. Quants also rely on the data-handling capabilities of MATLAB to customize and contextualize their pricing platform. For example, portfolio analysts use missing data functions to compensate for incomplete stock data, a common problem when running the CAPM model to value portfolios consisting of recent IPOs. With fixed income securities, analysts use MATLAB to build term structures of interest rates, fundamental to their valuation.
Optimizing execution speed for option pricing helps ensure a competitive edge in the market. Monte Carlo methods are ideal for distributing across clusters, processors, and cores, either managed from MATLAB directly or from deployed run-time versions, such as Excel add-ins or Web applications.