# Computational Finance

**Computational Finance**, a discipline that emerged in the 1980s, is also sometimes referred to as "financial engineering," "financial mathematics," "mathematical finance," or "quantitative finance." It uses the tools of mathematics, statistics, and computing to solve problems in finance. Computational methods and the mathematics behind them have become an indispensable part of the finance industry.

The industry to which computational finance is applied roughly divides into two parts, the sell side and the buy side. The sell side consists of the trading operations of investment banks that create and market a wide variety of financial products, including options, futures, and interest rate caps, floors, and swaps. Sometimes these investment banks are simply matching buy orders with sell orders, but often they are selling something that they have created and then they buy related instruments in order to be able to pay off on what they have sold if it becomes necessary. The buy side, on the other hand, is investing money by buying stocks, bonds, and the complicated products offered by the sell side.^{[1]}

The birth of computational finance as a discipline can be traced to Harry Markowitz in the early 1950s. Markowitz conceived of the portfolio selection problem as an exercise in mean-variance optimization. This required more computer power than was available at the time, so he worked on useful algorithms for approximate solutions. Mathematical finance began with the same insight but diverged by making simplifying assumptions to express relations in simple closed forms that did not require sophisticated computer science to evaluate.

In the 1960s, hedge fund managers such as Ed Thorp and Michael Goodkin (working with Harry Markowitz, Paul Samuelson, and Robert C. Merton) pioneered the use of computers in arbitrage trading. In academics, sophisticated computer processing was needed by researchers such as Eugene Fama in order to analyze large amounts of financial data in support of the efficient market hypothesis.

During the 1970s, the main focus of computational finance shifted to options pricing and analyzing mortgage securitizations. In the late 1970s and early 1980s, a group of young quantitative practitioners who became known as “rocket scientists” arrived on Wall Street and brought along personal computers. This led to an explosion of both the amount and variety of computational finance applications. Many of the new techniques came from signal processing and speech recognition rather than traditional fields of computational economics like optimization and time series analysis.

By the end of the 1980s, the winding down of the Cold War brought a large group of displaced physicists and applied mathematicians, many from behind the Iron Curtain, into finance. These people become known as “financial engineers” (“quant” is a term that includes both rocket scientists and financial engineers, as well as quantitative portfolio managers). This led to a second major extension of the range of computational methods used in finance, also a move away from personal computers to mainframes and supercomputers. Around this time computational finance became recognized as a distinct academic subfield. The first-degree program in computational finance was offered by Carnegie Mellon University in 1994.

Over the last 20 years, the field of computational finance has expanded into virtually every area of finance, and the demand for practitioners has grown dramatically. Moreover, many specialized companies have grown up to supply computational finance software and services.^{[2]}

## See Also

- Monte Carlo Method - A mathematical technique often used in computational finance for valuation and risk assessment.
- Black Scholes Model - A landmark mathematical model in financial markets for option pricing, which can be analyzed computationally.
- Risk Management - A broad financial discipline for identifying, assessing, and prioritizing risks, which computational finance often contributes to.