Computational finance


Computational finance

Computational finance, also called financial engineering, is a cross-disciplinary field which relies on computational intelligence, mathematical finance, numerical methods and computer simulations to make trading, hedging and investment decisions, as well as facilitating the risk management of those decisions. Utilising various methods, practitioners of computational finance aim to precisely determine the financial risk that certain financial instruments create.

Contents

History

Generally, individuals who fill positions in computational finance are known as “quants”, referring to the quantitative skills necessary to perform the job. Specifically, knowledge of the C++ programming language, as well as of the mathematical subfields of stochastic calculus, multivariate calculus, linear algebra, differential equations, probability theory and statistical inference are often entry level requisites for such a position. C++ has become the dominant language for two main reasons: the computationally intensive nature of many algorithms, and the focus on libraries rather than applications.

Computational finance was traditionally populated by Ph.Ds in finance, physics and mathematics who moved into the field from more pure, academic backgrounds (either directly from graduate school, or after teaching or research). To work in computational finance, one must have a strong understanding of financial economics, mathematical tools such as probability and statistics and differential equations, as well as engineering methodologies. However, as the actual use of computers has become essential to rapidly carrying out computational finance decisions, a background in computer programming has become useful, and hence many computer programmers enter the field either from Ph.D. programs or from other fields of software engineering. Specially with the advent of more complex computational machines, a knowledge of computer software and hardware has become a necessity. In recent years, advanced computational methods, such as neural network and evolutionary computation have opened new doors in computational finance. Practitioners of computational finance have come from the fields of signal processing and computational fluid dynamics and artificial intelligence. Masters level degree holders are also increasingly making their presence felt as more terminal programs become available at the leading schools; see Master of Computational Finance.

Today, all full service institutional finance firms employ computational finance professionals in their banking and finance operations (as opposed to being ancillary information technology specialists), while there are many other boutique firms ranging from 20 or fewer employees to several thousand that specialize in quantitative trading alone. JPMorgan Chase & Co. was one of the first firms to create a large derivatives business and employ computational finance (including through the formation of RiskMetrics), while Renaissance Technologies, founded in 1982, is probably the oldest and most notable quant fund (along with D.E. Shaw & Co.).

Applications

Computational finance is used in the creation of new financial instruments and strategies, typically exotic options and specialized interest rate derivatives. The field applies engineering methodologies to problems in finance, and employs financial theory and applied mathematics, as well as computation and the practice of programming.

Computational finance is also used in the process of creating new securities or processes, and designing new financial instruments, especially derivative securities. More importantly, computational finance is used in the process of employing mathematical, finance and computer modeling skills to make pricing, hedging, trading and portfolio management decisions. Utilizing various derivative securities and other methods, computational finance aims to precisely control the financial risk that an entity takes on. Methods can be employed to take on unlimited risks under certain events,or completely eliminate other risks by utilizing combinations of derivative and other securities.

Computational finance can be applied to many different types of currencies and pricing options. These include equity, fixed income such as bonds, commodities such as oil or gold, as well as derivatives, swaps, futures, forwards, options, and embedded options. With computational finance comes many risks. Risks are divided into market risk and credit risk. Market risks can be managed using risk identification, risk measurements, and risk management. Credit risks can be managed using credit modeling and credit pricing.

Computational finance is normally employed in the securities and banking industries. It is also used by quantitative analysts in consulting firms or in general manufacturing and service firms, in corporate treasury, corporate finance and risk management roles. Financial engineers will often hold doctorates in computer science or mathematics, although, increasingly, have instead completed a specialized (terminal) masters degree - usually the Master of Financial Engineering, or the more general Master of Quantitative Finance.

Areas where computational finance techniques are employed include:

Major contributors

Notable people in computational finance include F. Black and M. Scholes for the pricing of options and corporate liabilities, Robert C. Merton for his theory of rational option pricing and the introduction of stochastic calculus in the study of finance. Robert F. Engle is also notable for the work in analyzing economic time-series with time-varying volatility. Clive W. J. Granger analyzed the economic time series with common trend.

Some major contributors to computational finance include:

See also

External links


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