Who Is Robert F. Engle III?

Robert F. Engle III is an econometrician and professor of economics at New York University. Engle won the 2003 Nobel Prize in Economics, along with Clive W.J. Granger, for their analysis of time-series data with time-varying volatility.

Time-varying volatility is the fluctuation over time of the value of financial instruments, and Engle's discoveries of the variations in these instruments' volatility levels have become crucial tools for researchers and financial analysts. The model he developed is called autoregressive conditional heteroskedasticity (ARCH). He applied this model to understanding interest rate changes and volatility in asset prices.

Key Takeaways

  • Robert Engle is an econometrician and professor of economics at New York University who shared the 2003 Nobel Prize in Economics.
  • He is best known for his work on financial econometrics and the development of models to analyze financial time series data.
  • Engle famously developed autoregressive conditional heteroskedasticity (ARCH) modeling and testing.  
  • His work on ARCH, cointegration analysis, and other time-series econometric techniques helped found the field of financial econometrics, which forms the basis of much of modern quantitative financial practice. 
Robert F. Engle III

Investopedia / Alex Dos Diaz

Early Life and Education

Robert F. Engle III was born in 1942, in Syracuse, New York and earned his Ph.D. in economics from Cornell University. He has taught at the Massachusetts Institute of Technology (MIT), the University of California at San Diego, and New York University (NYU).

Originally, Dr. Engle's academic pursuit was physics (along with his doctorate degree in economics, he also earned a master's degree in physics at Cornell), but his love for economics led him to a career of research and teaching in the field. He credits Ta Chung Liu, his former advisor at Cornell, for grounding him in econometrics as well as sparking an intellectual interest in analyzing relationships between different time scales for economic modeling.

A fun fact about the man: Engle started ice skating as a hobby while in cold upstate New York and developed this passion to high skill levels, participating in numerous national adult skating competitions. He and his partners placed second in ice dancing in 1996 and 1999.

Notable Accomplishments

Engle is best known for his development of ARCH, for which he was awarded the Nobel Prize in economics. He has also done considerable work in econometric modeling for urban economics. Together with Clive Granger, he helped to develop a time-series econometric modeling of and tests for cointegration between series. He later extended these econometric techniques to help found the field of financial econometrics.

Urban Economics

Engle's early work was in urban economics at MIT, where he was part of a team that developed an elaborate econometric model of the Boston region economy. He published several articles about applying econometric modeling to urban economics to support urban planning and redevelopment with objective statistical tools, which was a novel approach at the time. 


Engle developed ARCH to model time-varying volatility in inflation, prices, and wages to test a theory of Milton Friedman's, which is that economic cycles could be explained based on changes over time in people's uncertainty about inflation. In ARCH modeling, the variance of the error term is modeled as a function of its own past values; if tests of this model show a significant relationship between the variance and its past values, then this indicates that the data in question exhibit some time periods of elevated volatility and other periods of relative calm.

The Nobel Committee awarded the prize to Dr. Engle, stating that "his method (ARCH) could, in particular, clarify market developments where turbulent periods, with large fluctuations, are followed by calmer periods, with modest fluctuations." 


While at UCSD with colleague Clive Granger, Engle helped to develop modeling techniques and tests for cointegration. In cointegration, two or more time series show a relationship through time somewhat similar to the correlation between cross-sectional variables. Cointegration analysis is one tool that can be used to help distinguish between variables that have a spurious correlation and those that have a plausible causal relationship.

Financial Econometrics  

Engle and others would go on to extend these time-series econometric techniques, along with others, to help found a new approach to financial forecasts, planning, and risk management, which became known as financial econometrics and quantitative finance. He was co-founder, along with Eric Ghysels, of the Society for Financial Econometrics.

Tools such as the capital asset pricing model, the value at risk model, and modern portfolio theory all fall under this general area. Much of modern quantitative finance owes its origins to the tools that Engle and other financial econometricians have developed. 

Why Did Robert F. Engle III Win the Nobel Prize in Economics?

Robert F. Engle III won the Nobel Prize in Economic Sciences in 2003 for his work on the concept of "volatility" in financial markets. Specifically, Engle was awarded the prize for his development of the ARCH (Autoregressive Conditional Heteroskedasticity) model, a statistical model used to analyze and predict the volatility of time series data, such as stock prices or exchange rates.

Engle's contributions to the field of financial economics have been recognized as groundbreaking and have had a lasting impact on the way that financial markets are analyzed and understood.

What Are ARCH Models Used for in Economics and Finance?

ARCH models, which stands for "Autoregressive Conditional Heteroskedasticity," are a type of statistical model used in economics and finance to analyze and predict the volatility of time series data, such as stock prices or exchange rates. These models are based on the idea that the variance of a time series is not constant, but rather varies over time in a predictable way.

One of the main applications of ARCH models is in financial risk management, where they are used to analyze and predict the likelihood of extreme movements in financial markets. For example, an investment manager might use an ARCH model to assess the risk associated with a particular portfolio of assets, or a financial institution might use an ARCH model to determine the capital requirements necessary to ensure the stability of its operations.

Why Is Modeling Volatility Important?

Modeling volatility is important for financial markets because it allows analysts and investors to better understand and predict the risk associated with various financial assets. Volatility refers to the degree to which the price of an asset fluctuates over time, and it is often used as a measure of risk. Higher volatility generally indicates higher risk, as there is a greater likelihood that the price of the asset will experience extreme movements, either up or down.

By modeling volatility, analysts and investors can better understand the risks associated with different assets and make more informed investment decisions. For example, if an analyst is trying to assess the risk associated with a particular portfolio of assets, they might use a volatility model to help identify which assets are most likely to experience extreme price movements. This can help them make more informed decisions about how to allocate their investment capital and manage risk.

In addition to its use in risk management, modeling volatility is also important for a variety of other purposes, including the development of financial instruments such as options and futures, the design of trading strategies, and the regulation of financial markets.

The Bottom Line

Robert F. Engle III is an American economist and Nobel laureate, known for his work on financial econometrics and the development of models to analyze financial time series data. He was awarded the Nobel Memorial Prize in Economic Sciences in 2003 for his contributions to the development of autoregressive conditional heteroskedasticity (ARCH) models, which are widely used in economics and finance to describe the volatility of financial asset returns. Engle is a professor emeritus at New York University's Stern School of Business, where he has taught since 2001. Prior to that, he held academic positions at the Massachusetts Institute of Technology, the University of California, San Diego, and other institutions.

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  1. The Nobel Prize. "Robert F. Engle III - Biographical."

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