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EspañolCOMPUTATIONAL FINANCE
(Redirected from Financial Engineering)
'Computational finance' (also known as 'financial engineering') is a cross-disciplinary field which relies on mathematical finance, numerical methods and computer simulations to make trading, hedging and investment decisions, as well as facilitating the risk management of those decisions. Utilizing various methods, practitioners of computational finance aim to precisely determine the financial risk that certain financial instruments create.
Areas where computational finance techniques are employed include:
★ Investment banking
★ Risk Management software
★ Corporate strategic planning
★ Securities trading and financial risk management
★ Derivatives trading and risk management
★ Investment management
Some major contributors to computational finance include:
★ Harry Markowitz
★ Myron Scholes
★ Robert C. Merton
★ Fischer Black
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) prior to the 1980s. However, as the actual use of computers has become essential to rapidly carrying out computational finance decisions, a background in pure computer science is now also needed, and hence many computing graduates enter the field as well. Masters level degree holders are also increasingly making their presence felt as more terminal programs become available at the leading schools (hence field practitioners are almost exclusively recruited).
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).
★ List of finance topics
★ Quantitative analyst
★ ActiveQuant
★ QuantLib
★ Mathematical finance
★ 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
'Computational finance' (also known as 'financial engineering') is a cross-disciplinary field which relies on mathematical finance, numerical methods and computer simulations to make trading, hedging and investment decisions, as well as facilitating the risk management of those decisions. Utilizing various methods, practitioners of computational finance aim to precisely determine the financial risk that certain financial instruments create.
| Contents |
| Areas of application |
| Major contributors |
| See also |
| External links |
Areas of application
Areas where computational finance techniques are employed include:
★ Investment banking
★ Risk Management software
★ Corporate strategic planning
★ Securities trading and financial risk management
★ Derivatives trading and risk management
★ Investment management
Major contributors
Some major contributors to computational finance include:
★ Harry Markowitz
★ Myron Scholes
★ Robert C. Merton
★ Fischer Black
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) prior to the 1980s. However, as the actual use of computers has become essential to rapidly carrying out computational finance decisions, a background in pure computer science is now also needed, and hence many computing graduates enter the field as well. Masters level degree holders are also increasingly making their presence felt as more terminal programs become available at the leading schools (hence field practitioners are almost exclusively recruited).
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).
See also
★ List of finance topics
★ Quantitative analyst
★ ActiveQuant
★ QuantLib
★ Mathematical finance
External links
★ 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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