Financial engineering

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The main applications of financial engineering [1] [2] are to:

Contents

Financial engineering is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming. [3] It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance. [4]

Financial engineering plays a key role in a bank's customer-driven derivatives business [5] — delivering bespoke OTC-contracts and "exotics", and implementing various structured products — which encompasses quantitative modelling, quantitative programming and risk managing financial products in compliance with the regulations and Basel capital/liquidity requirements.

An older use of the term "financial engineering" that is less common today is aggressive restructuring of corporate balance sheets.[ citation needed ] Mathematical finance is the application of mathematics to finance. [6] Computational finance and mathematical finance are both subfields of financial engineering.[ citation needed ] Computational finance is a field in computer science and deals with the data and algorithms that arise in financial modeling.

Discipline

Financial engineering draws on tools from applied mathematics, computer science, statistics and economic theory. [7] In the broadest sense, anyone who uses technical tools in finance could be called a financial engineer, for example any computer programmer in a bank or any statistician in a government economic bureau. [8] However, most practitioners restrict the term to someone educated in the full range of tools of modern finance and whose work is informed by financial theory. [9] It is sometimes restricted even further, to cover only those originating new financial products and strategies. [6]

Despite its name, financial engineering does not belong to any of the fields in traditional professional engineering even though many financial engineers have studied engineering beforehand and many universities offering a postgraduate degree in this field require applicants to have a background in engineering as well. [10] [11] In the United States, the Accreditation Board for Engineering and Technology (ABET) does not accredit financial engineering degrees. [12] In the United States, financial engineering programs are accredited by the International Association of Quantitative Finance. [13]

Quantitative analyst ("Quant") is a broad term that covers any person who uses math for practical purposes, including financial engineers. Quant is often taken to mean "financial quant", in which case it is similar to financial engineer. [14] The difference is that it is possible to be a theoretical quant, or a quant in only one specialized niche in finance, while "financial engineer" usually implies a practitioner with broad expertise. [15]

"Rocket scientist" (aerospace engineer) is an older term, first coined in the development of rockets in WWII (Wernher von Braun), and later, the NASA space program; it was adapted by the first generation of financial quants who arrived on Wall Street in the late 1970s and early 1980s. [16] While basically synonymous with financial engineer, it implies adventurousness and fondness for disruptive innovation. [17] Financial "rocket scientists" were usually trained in applied mathematics, statistics or finance and spent their entire careers in risk-taking. [18] They were not hired for their mathematical talents, they either worked for themselves or applied mathematical techniques to traditional financial jobs. [9] [17] The later generation of financial engineers were more likely to have PhDs in mathematics, physics, electrical and computer engineering, and often started their careers in academics or non-financial fields. [19] [20]

Criticisms

One of the prominent critics of financial engineering is Nassim Taleb, a professor of financial engineering at Polytechnic Institute of New York University [21] who argues that it replaces common sense and leads to disaster. A series of economic collapses has led many governments to argue a return to "real" engineering from financial engineering. A gentler criticism came from Emanuel Derman [22] who heads a financial engineering degree program at Columbia University. He blames over-reliance on models for financial problems; see Financial Modelers' Manifesto.

Many other authors have identified specific problems in financial engineering that caused catastrophes:

The financial innovation often associated with financial engineers was mocked by former chairman of the Federal Reserve Paul Volcker in 2009 when he said it was a code word for risky securities, that brought no benefits to society. For most people, he said, the advent of the ATM was more crucial than any asset-backed bond. [29]

Education

The first Master of Financial Engineering degree programs were set up in the early 1990s. The number and size of programs has grown rapidly, to the extent that some now use the term "financial engineer" to refer to a graduate in the field. [7] The financial engineering program at New York University Polytechnic School of Engineering was the first curriculum to be certified by the International Association of Financial Engineers. [30] [31] The number, and variation, of these programs has grown over the decades subsequent (see Master of Quantitative Finance § History); and lately includes undergraduate study, as well as designations such as the Certificate in Quantitative Finance.

See also

Related Research Articles

Finance is the study and discipline of money, currency and capital assets. It is related to and distinct from Economics which is the study of production, distribution, and consumption of goods and services. The discipline of Financial Economics bridges the two fields. Based on the scope of financial activities in financial systems, the discipline can be divided into personal, corporate, and public finance.

<span class="mw-page-title-main">Value at risk</span> Estimated potential loss for an investment under a given set of conditions

Value at risk (VaR) is a measure of the risk of loss of investment/Capital. It estimates how much a set of investments might lose, given normal market conditions, in a set time period such as a day. VaR is typically used by firms and regulators in the financial industry to gauge the amount of assets needed to cover possible losses.

<span class="mw-page-title-main">Nassim Nicholas Taleb</span> Lebanese-American author (born 1960)

Nassim Nicholas Taleb is a Lebanese-American essayist, mathematical statistician, former option trader, risk analyst, and aphorist whose work concerns problems of randomness, probability, and uncertainty.

Financial modeling is the task of building an abstract representation of a real world financial situation. This is a mathematical model designed to represent the performance of a financial asset or portfolio of a business, project, or any other investment.

<span class="mw-page-title-main">Computational finance</span>

Computational finance is a branch of applied computer science that deals with problems of practical interest in finance. Some slightly different definitions are the study of data and algorithms currently used in finance and the mathematics of computer programs that realize financial models or systems.

In mathematical finance, the Black–Derman–Toy model (BDT) is a popular short-rate model used in the pricing of bond options, swaptions and other interest rate derivatives; see Lattice model (finance) § Interest rate derivatives. It is a one-factor model; that is, a single stochastic factor—the short rate—determines the future evolution of all interest rates. It was the first model to combine the mean-reverting behaviour of the short rate with the log-normal distribution, and is still widely used.

<span class="mw-page-title-main">Emanuel Derman</span>

Emanuel Derman is a South African-born academic, businessman and writer. He is best known as a quantitative analyst, and author of the book My Life as a Quant: Reflections on Physics and Finance.

A master's degree in quantitative finance is a postgraduate degree focused on the application of mathematical methods to the solution of problems in financial economics. There are several like-titled degrees which may further focus on financial engineering, computational finance, mathematical finance, and/or financial risk management.

Jim Gatheral is a researcher in the field of mathematical finance, who has contributed to the study of volatility as applied to the pricing and risk management of derivatives. A recurrent subject in his books and papers is the volatility smile, and he published in 2006 a book The Volatility Surface based on a course he taught for six years at New York University, along with Nassim Taleb. More recently his work has moved in the direction of market microstructure, especially as applied to algorithmic trading. He is the author of The Volatility Surface: A Practitioner's Guide.

Neil A. Chriss is a mathematician, academic, hedge fund manager, philanthropist and a founding board member of the charity organization "Math for America" which seeks to improve math education in the United States. Chriss also serves on the board of trustees of the Institute for Advanced Study.

<span class="mw-page-title-main">Paul Wilmott</span>

Paul Wilmott is an English researcher, consultant and lecturer in quantitative finance. He is best known as the author of various academic and practitioner texts on risk and derivatives, for Wilmott magazine and Wilmott.com, a quantitative finance portal, and for his prescient warnings about the misuse of mathematics in finance.

Aaron C. Brown is an American finance practitioner, well known as an author on risk management and gambling-related issues. He also speaks frequently at professional and academic conferences. He was Chief Risk Manager at AQR Capital Management. He was one of the original developers of value at risk and one of its strongest proponents.

In finance, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card transactions, and computing the probability of air flight passenger being a terrorist. Rebonato in 2002 defines model risk as "the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the price at which the same instrument is revealed to have traded in the market".

The Financial Modelers' Manifesto was a proposal for more responsibility in risk management and quantitative finance written by financial engineers Emanuel Derman and Paul Wilmott. The manifesto includes a Modelers' Hippocratic Oath. The structure of the Financial Modelers' Manifesto mirrors that of The Communist Manifesto of 1848.

Scott Patterson is an American financial journalist and bestselling author. He is a staff reporter at The Wall Street Journal and author of Dark Pools: High-Speed Traders, A.I. Bandits, and the Threat to the Global Financial System and The New York Times bestselling bookThe Quants.

<i>The Quants</i> 2010 book by Scott Patterson

The Quants is the debut New York Times best selling book by Wall Street journalist Scott Patterson. It was released on February 2, 2010 by Crown Business. The book describes the world of quantitative analysis and the various hedge funds that use the technique. Two years later, Patterson published a follow-up book, Dark Pools: High Speed Traders, AI Bandits and the Threat to the Global Financial System, an investigative journey into the history of high-frequency trading and the spread of artificial intelligence in today’s markets.

Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. Those working in the field are quantitative analysts (quants). Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, investment management and other related finance occupations. The occupation is similar to those in industrial mathematics in other industries. The process usually consists of searching vast databases for patterns, such as correlations among liquid assets or price-movement patterns.

Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling of financial markets.

<span class="mw-page-title-main">Rocket science (finance)</span>

"Rocket science" in finance is a metaphor for activity carried out by specialised quantitative staff to provide detailed output from mathematical modeling and computational simulations to support investment decisions. Their work depends on use of complex mathematical models implemented in sophisticated IT environments.

References

  1. ↑ Marek Capiski and Tomasz Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, Springer (November 25, 2010) 978-0857290816
  2. ↑ David Ruppert, Statistics and Data Analysis for Financial Engineering, Springer (November 17, 2010) 978-1441977861
  3. ↑ "MS in Financial Engineering". Columbia University Department of Industrial Engineering and Operations Research. Archived from the original on 2017-01-19. Retrieved 2017-01-18.
  4. ↑ Tanya S. Beder and Cara M. Marshall, Financial Engineering: The Evolution of a Profession, Wiley (June 7, 2011) 978-0470455814
  5. ↑ Qu, Dong (2016). Manufacturing and Managing Customer-Driven Derivatives . Wiley. ISBN   978-1-118-63262-8.
  6. 1 2 Robert Dubil, Financial Engineering and Arbitrage in the Financial Markets, Wiley (October 11, 2011) 978-0470746011
  7. 1 2 "What is Financial Engineering?". International Association of Financial Engineers. Archived from the original on 2012-06-30. Retrieved 2012-07-22.
  8. ↑ Ali N. Akansu and Mustafa U. Torun. (2015), A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading, Boston, MA: Academic Press, ISBN   978-0-12-801561-2
  9. 1 2 Salih N. Neftci, Principles of Financial Engineering, Academic Press (December 15, 2008) 978-0123735744
  10. ↑ Entry requirements | Imperial College Business School. 2016. Entry requirements | Imperial College Business School. [ONLINE] Available at: http://wwwf.imperial.ac.uk/business-school/programmes/msc-risk-management/entry-requirements/ Archived 2016-06-27 at the Wayback Machine . [Accessed 30 June 2016]. Add to My References
  11. ↑ "Master Financial Engineering postgraduate distance learning-TU Kaiserslautern".
  12. ↑ "List of Member Societies". ABET. Archived from the original on 2013-04-30. Retrieved 26 April 2013.
  13. ↑ "{title}". Archived from the original on 2018-06-13. Retrieved 2018-08-21.
  14. ↑ Espen Gaarder Haug, Derivatives Models on Models, Wiley (July 24, 2007) 978-0470013229
  15. ↑ Richard R. Lindsey and Barry Schachter (editors), How I Became a Quant: Insights from 25 of Wall Street's Elite, Wiley (August 3, 2009) 978-0470452578
  16. ↑ Emanuel Derman, My Life as a Quant: Reflections on Physics and Finance, Wiley (September 16, 2004) 978-0471394204
  17. 1 2 Aaron Brown, Red-Blooded Risk: The Secret History of Wall Street, Wiley (October 11, 2011) 978-1118043868
  18. ↑ Aaron Brown, The Poker Face of Wall Street, Wiley (March 31, 2006) 978-0470127315
  19. ↑ Dan Stefanica, A Primer for the Mathematics of Financial Engineering, FE Press (April 4, 2008) 978-0979757600
  20. ↑ Akansu, Ali N.; Kulkarni, Sanjeev R.; Malioutov, Dmitry M., Eds. (2016), Financial Signal Processing and Machine Learning, Hoboken, NJ: Wiley-IEEE Press, ISBN   978-1-118-74567-0
  21. ↑ Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, Random House (April 17, 2007) 978-1400063512
  22. ↑ Emanuel Derman, Models.Behaving.Badly.: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life, Free Press (July 24, 2012) 978-1439164990
  23. ↑ "Whodunit? Rocket Scientists on Wall Street". Minyanville. Archived from the original on 2012-07-11. Retrieved 2012-07-22.
  24. ↑ "Recipe for Disaster: The Formula that Killed Wall Street". Wired. February 23, 2009. Archived from the original on 2012-07-26. Retrieved 2012-07-22.
  25. ↑ Stewart, Ian (February 12, 2012). "The Mathematical Equation that Caused the Banks to Crash". London: Wired. Archived from the original on 2013-09-27. Retrieved 2012-07-22.
  26. ↑ < Pablo Triana, The Number That Killed Us: A Story of Modern Banking, Flawed Mathematics, and a Big Financial Crisis , Wiley (December 6, 2011) 978-0470529737
  27. ↑ < Scott Patterson, The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It, Crown Business (February 2, 2010) 978-0307453372
  28. ↑ < Scott Patterson, Dark Pools: High-Speed Traders, A.I. Bandits, and the Threat to the Global Financial System, Crown Business (June 12, 2012) 978-0307887177
  29. ↑ "Crisis may be worse than Depression, Volcker says". Reuters. Feb 20, 2009. Archived from the original on 2013-09-28. Retrieved 2013-09-05.
  30. ↑ "{title}". Archived from the original on 2013-04-10. Retrieved 2013-04-25.
  31. ↑ "The Department of Finance and Risk Engineering". Polytechnic Institute of NYU. Archived from the original on 2014-01-04. Retrieved 2012-05-09.

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