Financial engineering is a multidisciplinary field involving financial theory, methods of engineering, tools of mathematics and the practice of programming.It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance. 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. In the United States, the Accreditation Board for Engineering and Technology (ABET) does not accredit financial engineering degrees. In the United States, financial engineering programs are accredited by the International Association of Quantitative Finance.
Mathematical finance, also known as quantitative finance, is a field of applied mathematics, concerned with mathematical modeling of financial markets. Generally, mathematical finance will derive and extend the mathematical or numerical models without necessarily establishing a link to financial theory, taking observed market prices as input. Mathematical consistency is required, not compatibility with economic theory. Thus, for example, while a financial economist might study the structural reasons why a company may have a certain share price, a financial mathematician may take the share price as a given, and attempt to use stochastic calculus to obtain the corresponding value of derivatives of the stock. The fundamental theorem of arbitrage-free pricing is one of the key theorems in mathematical finance, while the Black–Scholes equation and formula are amongst the key results.
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.
Finance is a field that is concerned with the allocation (investment) of assets and liabilities over space and time, often under conditions of risk or uncertainty. Finance can also be defined as the art of money management. Participants in the market aim to price assets based on their risk level, fundamental value, and their expected rate of return. Finance can be split into three sub-categories: public finance, corporate finance and personal finance.
Financial engineering draws on tools from applied mathematics, computer science, statistics and economic theory.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. 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. It is sometimes restricted even further, to cover only those originating new financial products and strategies. Financial engineering plays a key role in the customer-driven derivatives business which encompasses quantitative modelling and programming, trading and risk managing derivative products in compliance with the regulations and Basel capital/liquidity requirements.
Applied mathematics is the application of mathematical methods by different fields such as science, engineering, business, computer science, and industry. Thus, applied mathematics is a combination of mathematical science and specialized knowledge. The term "applied mathematics" also describes the professional specialty in which mathematicians work on practical problems by formulating and studying mathematical models. In the past, practical applications have motivated the development of mathematical theories, which then became the subject of study in pure mathematics where abstract concepts are studied for their own sake. The activity of applied mathematics is thus intimately connected with research in pure mathematics.
Computer science is the study of processes that interact with data and that can be represented as data in the form of programs. It enables the use of algorithms to manipulate, store, and communicate digital information. A computer scientist studies the theory of computation and the practice of designing software systems.
Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics.
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.
Computational finance and mathematical finance are both subfields of financial engineering. Computational finance is a field in computer science and deals with the data and algorithms that arise in financial modeling. Mathematical finance is the application of mathematics to finance.
Mathematics includes the study of such topics as quantity, structure, space, and change.
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.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.
A quantitative analyst is a person who specializes in the application of mathematical and statistical methods to financial and risk management problems. The occupation is similar to those in industrial mathematics in other industries.
“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.While basically synonymous with financial engineer, it implies adventurousness and fondness for disruptive innovation. Financial "Rocket scientists" were usually trained in applied mathematics, statistics or finance; and spent their entire careers in risk-taking. They were not hired for their mathematical talents, they either worked for themselves or applied mathematical techniques to traditional financial jobs. The later generation of financial engineers were more likely to have PhDs in mathematics or physics and often started their careers in academics or non-financial fields.
Wernher Magnus Maximilian Freiherr von Braun was a German-American aerospace engineer and space architect. He was the leading figure in the development of rocket technology in Germany and a pioneer of rocket technology and space science in the United States.
Wall Street is an eight-block-long street running roughly northwest to southeast from Broadway to South Street, at the East River, in the Financial District of Lower Manhattan in New York City. Over time, the term has become a metonym for the financial markets of the United States as a whole, the American financial services industry, or New York–based financial interests.
In business theory, a disruptive innovation is an innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market-leading firms, products, and alliances. The term was defined and first analyzed by the American scholar Clayton M. Christensen and his collaborators beginning in 1995, and has been called the most influential business idea of the early 21st century.
The first degree programs in financial engineering were set up in the early 1990s. The number and size of programs has grown rapidly, so now some people use the term “financial engineer” to mean someone who has a degree in the field.
An older use of the term "financial engineering" that is less common today is aggressive restructuring of corporate balance sheets. It is generally (but not always) a disparaging term, implying that someone is profiting from paper games at the expense of employees and investors.
The main applications of financial engineeringare to:
One of the critics of financial engineering is Nassim Taleb, a professor of financial engineering at Polytechnic Institute of New York Universitywho 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.
Many other authors have identified specific problems in financial engineering that caused catastrophes: Aaron Brownnamed confusion between quants and regulators over the meaning of “capital”, Felix Salmon gently pointed to the Gaussian copula, Ian Stewart criticized the Black-Scholes formula, Pablo Triana dislikes value at risk and Scott Patterson accused quantitative traders and later high-frequency traders.
A gentler criticism came from Emanuel Dermanwho heads a financial engineering degree program at Columbia University. He blames over-reliance on models for financial problems.
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.
Beder, Tanya S.; Marshall, Cara M. (2011). Financial Engineering: The Evolution of a Profession. John Wiley & Sons.
In finance, statistical arbitrage is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities held for short periods of time. These strategies are supported by substantial mathematical, computational, and trading platforms.
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.
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 lognormal distribution, and is still widely used.
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 masters degree in quantitative finance concerns 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, financial risk management, computational finance and/or mathematical finance.
Mark Edward Rubinstein is a leading financial economist and financial engineer. He is Professor of Finance at the Haas School of Business of the University of California, Berkeley.
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.
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.
Bruno Dupire is a researcher and lecturer in quantitative finance. He is currently Head of Quantitative Research at Bloomberg LP. He is best known for his contributions to local volatility modeling and Functional Ito Calculus. He is also an Instructor at New York University since 2005, in the Courant Master of Science Program in Mathematics in Finance.
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.
A master's degree in Financial Economics provides a rigorous understanding of theoretical finance and the economic framework upon which that theory is based. The degree is postgraduate, and usually incorporates a thesis or research component. Programs may be offered jointly by the business school and the economics department.
Piotr Karasinski is a pioneering quantitative analyst, best known for the Black–Karasinski short rate model which he co-developed with the late Fischer Black. His contributions to quantitative finance include models for interest rates, equity and hybrid products and random volatility.
"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.