Computational finance
From Wikipedia, the free encyclopedia
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.
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[edit] Areas of application
Areas where computational finance techniques are employed include:
- Investment banking
- Corporate strategic planning
- Securities trading and financial risk management
- Derivatives trading and risk management
- Investment management
[edit] Major contributors
Some major contributors to computational finance include:
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: 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).
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EducationI
- Master of Science in Applied Math (Mathematical Finance) at University of Calgary, Calgary, AB, Canada
- The Masters in Financial Engineering (MFE) at Baruch College, New York, NY, USA
- Master's in Financial Engineering at University of California, Berkeley, Berkeley, CA, USA.
- Master's degree in Mathematical Finance at Boston University, Boston, MA, USA.
- Master's and Ph.D. degrees with Computational Finance specialization at Purdue University, IN, USA
- Master of Quantitative and Computational Finance at Georgia Institute of Technology, GA, USA
- Master of Advanced Studies in Finance at Swiss Federal Institute of Technology Zurich (ETH), Switzerland
- MSc Quantitative Finance and Financial Engineering at Manchester University, UK
- Center for Research in Financial Mathematics and Statistics (CRFMS) at University of California-Santa Barbara
- Master in Financial Engineering at Illinois Institute of Technology, Chicago, IL
- The Masters in Mathematics in Finance program at New York university's Courant Institute of Mathematical Sciences
- The Masters in Computational Finance (MSCF) program at Carnegie Mellon University was the first degree offered of its kind.
- The Masters in Financial Engineering (MFE) program at Nanyang Technological University, Singapore.
- The Quantitative Finance Research Centre at the University of Technology, Sydney
- Master of Mathematical Finance at the University of Toronto, Toronto, Canada
- Master of Science in Computational Finance at CTI DePaul University, Chicago, Il
- Master in Financial Engineering at Polytechnic University, New York, NY
- Master of Science in Financial Engineering at De La Salle University
- Master of Science in Financial Engineering at Stevens Institute of Technology, Hoboken, NJ
- Master and PhD degrees in Computational Finance at Centre for Computational Finance and Economic Agents (CCFEA), University of Essex, UK
Other
- Financial Mathematics, Computational finance and Risk Management
- An Introduction to Computational Finance without Agonizing Pain
- Quantlib, a C++ library for quantitative finance
- Quant Equation Archive at Sitmo: equations for financial engineers
- vbnumericalmethods.com Excel VBA code for finance applications.
- Computational Finance and Economics Network (CFETC) at IEEE Computational Intelligence Society
- Introduction to Computational Finance, IEEE Computational Intelligence Society Newsletter, August 2004
- Numerical Techniques for Options
- Monte Carlo Simulation of Stochastic Processes
| General areas of finance |
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| Financial markets • Investment management • Financial institutions • Personal finance • Public finance • Mathematical finance • Financial economics • Experimental finance • Computational finance |
