Robert F. Engle III | |
---|---|

Born | Syracuse, New York, U.S. | November 10, 1942

Education | Williams College (BS) Cornell University (MS, PhD) |

Academic career | |

Institution | New York University, since 2000 University of California, San Diego, (1975–2003) Massachusetts Institute of Technology, (1969–1975) |

Field | Econometrics |

Doctoral advisor | Ta-Chung Liu ^{ [1] } |

Doctoral students | Mark Watson Tim Bollerslev |

Influences | David Hendry |

Contributions | ARCH Cointegration |

Awards | Nobel Memorial Prize in Economic Sciences (2003) |

Information at IDEAS / RePEc | |

Academic background | |

Thesis | Biases From Time-Aggregation of Distributed Lag Models (1969) |

**Robert Fry Engle III** (born November 10, 1942) is an American economist and statistician. He won the 2003 Nobel Memorial Prize in Economic Sciences, sharing the award with Clive Granger, "for methods of analyzing economic time series with time-varying volatility (ARCH)".

Engle was born in Syracuse, New York into a Quaker family^{ [2] } and went on to graduate from Williams College with a BS in physics. He earned an MS in physics and a PhD in economics, both from Cornell University, in 1966 and 1969 respectively.^{ [3] } After completing his PhD, Engle became an economics professor at the Massachusetts Institute of Technology from 1969 to 1977.^{ [4] } He joined the faculty of the University of California, San Diego (UCSD) in 1975, wherefrom he retired in 2003. He now holds positions of Professor Emeritus and Research Professor at UCSD. He currently teaches at New York University, Stern School of Business where he is the Michael Armellino professor in Management of Financial Services. At New York University, Engle teaches for the Master of Science in Risk Management Program for Executives.^{ [5] }^{ [6] }

Engle's most important contribution was his path-breaking discovery of a method for analyzing unpredictable movements in financial market prices and interest rates. Accurate characterization and prediction of these volatile movements are essential for quantifying and effectively managing risk. For example, risk measurement plays a key role in pricing options and financial derivatives. Previous researchers had either assumed constant volatility or had used simple devices to approximate it. Engle developed new statistical models of volatility that captured the tendency of stock prices and other financial variables to move between high volatility and low volatility periods ("Autoregressive Conditional Heteroskedasticity: ARCH"). These statistical models have become essential tools of modern arbitrage pricing theory and practice.

Engle was the central founder and director of NYU-Stern's Volatility Institute which publishes weekly date on systemic risk across countries on its V-LAB site.^{ [7] }^{ [8] } He was awarded a Doctor Honoris Causa by the Comillas Pontifical University in Spain in 2024.^{ [9] }

- Engle, Robert F. (1982). "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation".
*Econometrica*.**50**(4): 987–1008. doi:10.2307/1912773. JSTOR 1912773. - Engle, Robert F.; Hendry, David F.; Richard, Jean-Francois (1983). "Exogeneity".
*Econometrica*.**51**(2). (with David F. Hendry and Jean-Francois Richard): 277–304. doi:10.2307/1911990. JSTOR 1911990. - "Semi-parametric Estimates of the Relation between Weather and Electricity Demand".
*J. Amer. Statist. Assoc.***81**(394). (with C. Granger, J. Rice and A. Weiss): 310–320. 1986. doi:10.1080/01621459.1986.10478274.`{{cite journal}}`

: CS1 maint: others (link) - Engle, Robert F.; Granger, C. W. J. (1987). "Co-Integration and Error Correction: Representation, Estimation, and Testing" (PDF).
*Econometrica*.**55**(2). (with Clive Granger): 251–276. doi:10.2307/1913236. JSTOR 1913236. S2CID 16616066. - Engle, Robert F.; Lilien, David M.; Robins, Russell P. (1987). "Estimation of Time Varying Risk Premia in the Term Structure: the ARCH-M Model".
*Econometrica*.**55**(2). (with David Lilien and Russell Robins): 391–407. doi:10.2307/1913242. JSTOR 1913242. - "Asset Pricing with a Factor ARCH Covariance Structure: Empirical Estimates for Treasury Bills" (PDF).
*Journal of Econometrics*.**45**(1–2). (with V. Ng, and M. Rothschild): 213–237. 1990. doi:10.1016/0304-4076(90)90099-F. hdl: 2027.42/28496 . S2CID 55667632.`{{cite journal}}`

: CS1 maint: others (link) - Engle, Robert F.; Russell, Jeffrey R. (1998). "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data".
*Econometrica*.**66**(5). (with J.R. Russell): 1127–1162. doi:10.2307/2999632. JSTOR 2999632. - "Dynamic Conditional Correlation – A Simple Class of Multivariate GARCH Models".
*Journal of Business and Economic Statistics*.**20**(3): 339–350. 2002. doi:10.1198/073500102288618487. S2CID 14784060. - Easley, D.; Engle, R. F.; O'Hara, M.; Wu, L. (2008). "Time-Varying Arrival Rates of Informed and Uninformed Traders".
*Journal of Financial Econometrics*.**6**(2). (with Maureen O'Hara, David Easley and L. Wu): 171–207. doi: 10.1093/jjfinec/nbn003 .

* Econometrica* is a peer-reviewed academic journal of economics, publishing articles in many areas of economics, especially econometrics. It is published by Wiley-Blackwell on behalf of the Econometric Society. The current editor-in-chief is Guido Imbens.

**Sir Clive William John Granger** was a British econometrician known for his contributions to nonlinear time series analysis. He taught in Britain, at the University of Nottingham and in the United States, at the University of California, San Diego. Granger was awarded the Nobel Memorial Prize in Economic Sciences in 2003 in recognition of the contributions that he and his co-winner, Robert F. Engle, had made to the analysis of time series data. This work fundamentally changed the way in which economists analyse financial and macroeconomic data.

In econometrics, the **autoregressive conditional heteroskedasticity** (**ARCH**) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; often the variance is related to the squares of the previous innovations. The ARCH model is appropriate when the error variance in a time series follows an autoregressive (AR) model; if an autoregressive moving average (ARMA) model is assumed for the error variance, the model is a **generalized autoregressive conditional heteroskedasticity** (**GARCH**) model.

In finance, **volatility clustering** refers to the observation, first noted by Mandelbrot (1963), that "large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes." A quantitative manifestation of this fact is that, while returns themselves are uncorrelated, absolute returns or their squares display a positive, significant and slowly decaying autocorrelation function: corr(|r_{t}|, |r_{t+τ} |) > 0 for τ ranging from a few minutes to several weeks. This empirical property has been documented in the 90's by Granger and Ding (1993) and Ding and Granger (1996) among others; see also. Some studies point further to long-range dependence in volatility time series, see Ding, Granger and Engle (1993) and Barndorff-Nielsen and Shephard.

**Financial econometrics** is the application of statistical methods to financial market data. Financial econometrics is a branch of financial economics, in the field of economics. Areas of study include capital markets, financial institutions, corporate finance and corporate governance. Topics often revolve around asset valuation of individual stocks, bonds, derivatives, currencies and other financial instruments.

**Cointegration** is a statistical property of a collection (*X*_{1}, *X*_{2}, ..., *X*_{k}) of time series variables. First, all of the series must be integrated of order *d* (see Order of integration). Next, if a linear combination of this collection is integrated of order less than d, then the collection is said to be co-integrated. Formally, if (*X*,*Y*,*Z*) are each integrated of order *d*, and there exist coefficients *a*,*b*,*c* such that *aX* + *bY* + *cZ* is integrated of order less than d, then *X*, *Y*, and *Z* are cointegrated. Cointegration has become an important property in contemporary time series analysis. Time series often have trends—either deterministic or stochastic. In an influential paper, Charles Nelson and Charles Plosser (1982) provided statistical evidence that many US macroeconomic time series (like GNP, wages, employment, etc.) have stochastic trends.

In statistics, **homogeneity** and its opposite, **heterogeneity**, arise in describing the properties of a dataset, or several datasets. They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part. In meta-analysis, which combines the data from several studies, homogeneity measures the differences or similarities between the several studies.

**Lars Peter Hansen** is an American economist. He is the David Rockefeller Distinguished Service Professor in Economics, Statistics, and the Booth School of Business, at the University of Chicago and a 2013 recipient of the Nobel Memorial Prize in Economics.

**Christian Gouriéroux** is an econometrician who holds a Doctor of Philosophy in mathematics from the University of Rouen. He has the Professor exceptional level title from France. Gouriéroux is now a professor at University of Toronto and CREST, Paris [Center for Research in Economics and Statistics].

**Tim Peter Bollerslev** is a Danish economist, currently the *Juanita and Clifton Kreps Professor of Economics* at Duke University. A fellow of the Econometric Society, Bollerslev is known for his ideas for measuring and forecasting financial market volatility and for the GARCH model.

**Sir David Forbes Hendry**, FBA CStat is a British econometrician, currently a professor of economics and from 2001 to 2007 was head of the economics department at the University of Oxford. He is also a professorial fellow at Nuffield College, Oxford.

In financial econometrics, an **autoregressive conditional duration** model considers irregularly spaced and autocorrelated intertrade durations. ACD is analogous to GARCH. In a continuous double auction waiting times between two consecutive trades vary at random.

**John Denis Sargan**, FBA was a British econometrician who specialized in the analysis of economic time-series.

**Anil K. Bera** is an Indian-American econometrician. He is Professor of Economics at University of Illinois at Urbana–Champaign's Department of Economics. He is most noted for his work with Carlos Jarque on the Jarque–Bera test.

The **LSE approach to econometrics**, named for the London School of Economics, involves viewing econometric models as *reductions* from some unknown data generation process (DGP). A complex DGP is typically modelled as the starting point and this complexity allows information in the data from the real world but absent in the theory to be drawn upon. The complexity is then reduced by the econometrician by a series of restrictions which are tested.

**William H. Greene** is an American economist. He was formerly the Robert Stansky Professor of Economics and Statistics at Stern School of Business at New York University. Greene is currently a Visiting Professor of Economics at the University of South Florida.

**Francis X. Diebold** is an American economist known for his work in predictive econometric modeling, financial econometrics, and macroeconometrics. He earned both his B.S. and Ph.D. degrees at the University of Pennsylvania, where his doctoral committee included Marc Nerlove, Lawrence Klein, and Peter Pauly. He has spent most of his career at Penn, where he has mentored approximately 75 Ph.D. students. Presently he is Paul F. and Warren S. Miller Professor of Social Sciences and Professor of Economics at Penn’s School of Arts and Sciences, and Professor of Finance and Professor of Statistics at Penn’s Wharton School. He is also a Faculty Research Associate at the National Bureau of Economic Research in Cambridge, Massachusetts, and author of the *No Hesitations* blog.

**High frequency data** refers to time-series data collected at an extremely fine scale. As a result of advanced computational power in recent decades, high frequency data can be accurately collected at an efficient rate for analysis. Largely used in the financial field, high frequency data provides observations at very frequent intervals that can be used to understand market behaviors, dynamics, and micro-structures.

**Richard T. Baillie** is a British–American economist and statistician who is currently the A J Pasant Professor of Economics at the Michigan State University. He is also part time professor at King's College, London, and Senior Scientific Officer for the Rimini Center for Economic Analysis in Italy, and also on the Executive Council of the Society for Nonlinear Dynamics in Econometrics (SNDE).

In statistics, a sequence of random variables is **homoscedastic** if all its random variables have the same finite variance; this is also known as **homogeneity of variance**. The complementary notion is called **heteroscedasticity**, also known as **heterogeneity of variance**. The spellings *homos kedasticity* and

- ↑ Engle, Robert F.; Liu, Ta-Chung (1972), "Effects of Aggregation Over Time on Dynamic Characteristics of An Econometric Model", in Hickman, Bert G. (ed.),
*Econometric Models of Cyclical Behavior*(PDF), Conference on Research in Income and Wealth. Studies in income and wealth, vol. 2, NBER, p. 673. - ↑ Robert F. Engle III on Nobelprize.org , accessed 2 May 2020
- ↑ Homepage at New York University
- ↑ MIT Nobel laureates
- ↑ "NYU Stern School of Business" . Retrieved 10 March 2017.
- ↑ "Amsterdam Institute of Finance – Financial Training" . Retrieved 10 March 2017.
- ↑ The Volatility Institute at NYU-Stern School of Business site
- ↑ Engle, Robert (2022). Farmer, Doyne; Kleinnijenhuis, Alissa; Schuermann, Til; Wetzer, Thom (eds.).
*Stress Testing with Market Data*. Cambridge University Press. p. 142–161. - ↑ "Dos honoris causa que estudian la relación entre cambio climático y finanzas". Comillas Pontifical University. 2024.

- V-Lab: real time financial volatility and correlation measurements, modeling and forecasting
- The Society for Financial Econometrics (SoFiE)
- "Robert F. Engle (1942– )".
*The Concise Encyclopedia of Economics*. Library of Economics and Liberty (2nd ed.). Liberty Fund. 2008. - Robert F. Engle at the Mathematics Genealogy Project
- Appearances on C-SPAN

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