Computational finance
From Wikipedia, the free encyclopedia
Computational finance or 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.
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[edit] 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). 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. 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. 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 D. E. Shaw & Co. is probably the oldest and largest quant fund (Citadel Investment Group is a major rival).
[edit] Areas of application
Areas where computational finance techniques are employed include:
- Investment banking
- Forecasting
- Risk Management software
- Corporate strategic planning
- Securities trading and financial risk management
- Derivatives trading and risk management
- Investment management
- Pension scheme
- Insurance policy
- Mortgage agreement
- Lottery design
- Islamic banking
- Currency peg
- Gold and commodity valuation
- Collateralised debt obligation
- Credit default swap
- Bargaining
- Market mechanism design
[edit] Major contributors
Some major contributors to computational finance include:
- Fischer Black
- Phelim Boyle
- Emanuel Derman
- Robert Jarrow
- Harry Markowitz
- Robert C. Merton
- Stephen Ross
- Myron Scholes
- Edward Tsang
[edit] See also
[edit] External links
- IEEE Computational Finance and Economics Technical Committee
- An Introduction to Computational Finance without Agonizing Pain
- Introduction to Computational Finance, IEEE Computational Intelligence Society Newsletter, August 2004
- Numerical Techniques for Options
- Monte Carlo Simulation of Stochastic Processes
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