Robert Engle
Biographic Data
| ID | 1126626 |
|---|---|
| NAME | Robert Engle |
| GIVEN NAMES | Robert |
| FAMILY NAME | Engle |
| SIGNATURE | ENGLE R |
| AFFILIATIONS | New York University |
| VERIFIED | No |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 22 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 1 |
| FIRST PUBLICATION YEAR | 1979 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 2 |
Fitting Vast Dimensional Time-Varying Covariance Models
Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds.…
Dynamic Equicorrelation
A new covariance matrix estimator is proposed under the assumption that at every time period all pairwise correlations are equal. This assumption, which is pragmatically applied in various areas of finance, makes it possible to estimate arbitrarily large covariance matrices with ease. The model, called DECO, involves first adjusting for individual volatilities and then estimating correlations. A quasi-maximum likelihood result shows that DECO pro…
Testing and Valuing Dynamic Correlations for Asset Allocation
We evaluate alternative models of variances and correlations with an economic loss function. We construct portfolios to minimize predicted variance subject to a required return. It is shown that the realized volatility is smallest for the correctly specified covariance matrix for any vector of expected returns. A test of relative performance of two covariance matrices is based on work of Diebold and Mariano. The method is applied to stocks and bo…
Dynamic Conditional Correlation
Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be est…
Garch 101
ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. This paper gives the motivation behind the simplest GARCH model and illustrates its usefulness in examining portfolio risk. Extensions are briefly discussed
Handbook of Econometrics Volume 4 (Handbooks in Economics) (Handbook of Econometrics)
Physician Frustration in Communicating with Patients
In this study, the nature of practicing physicians' "frustrating" visits was explored and a guide to help physicians identify problems in communicating with patients was developed. The study included 1,076 practicing physicians who attended a voluntary workshop on physician-patient communication. The physicians were from multiple specialties and diverse work settings geographically dispersed throughout the United States. The method included devel…
On the theory of growth controls
The Econometrics of Panel Data. Annales de l'insee 30-31
Journal Article The Econometrics of Panel Data. Annates de I'insee 30-31 Get access The Econometrics of Panel Data. Annates de I'insee 30-31. Edited by PASCAL MAZODIER. (Paris: INSEE, 1978. Pp. 747, $18.00, FF 60.) Robert Engle Robert Engle University of California, San Diego, and London School of Economics Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 89, Issue 356, 1 December 1979, Pages 9…
On the theory of growth controls
Garch 101
ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. This paper gives the motivation behind the simplest GARCH model and illustrates its usefulness in examining portfolio risk. Extensions are briefly discussed
The Econometrics of Panel Data. Annales de l'insee 30-31
Journal Article The Econometrics of Panel Data. Annates de I'insee 30-31 Get access The Econometrics of Panel Data. Annates de I'insee 30-31. Edited by PASCAL MAZODIER. (Paris: INSEE, 1978. Pp. 747, $18.00, FF 60.) Robert Engle Robert Engle University of California, San Diego, and London School of Economics Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 89, Issue 356, 1 December 1979, Pages 9…
On the theory of growth controls
Physician Frustration in Communicating with Patients
In this study, the nature of practicing physicians' "frustrating" visits was explored and a guide to help physicians identify problems in communicating with patients was developed. The study included 1,076 practicing physicians who attended a voluntary workshop on physician-patient communication. The physicians were from multiple specialties and diverse work settings geographically dispersed throughout the United States. The method included devel…
Handbook of Econometrics Volume 4 (Handbooks in Economics) (Handbook of Econometrics)
Garch 101
ARCH and GARCH models have become important tools in the analysis of time series data, particularly in financial applications. These models are especially useful when the goal of the study is to analyze and forecast volatility. This paper gives the motivation behind the simplest GARCH model and illustrates its usefulness in examining portfolio risk. Extensions are briefly discussed
Dynamic Conditional Correlation
Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be est…
Testing and Valuing Dynamic Correlations for Asset Allocation
We evaluate alternative models of variances and correlations with an economic loss function. We construct portfolios to minimize predicted variance subject to a required return. It is shown that the realized volatility is smallest for the correctly specified covariance matrix for any vector of expected returns. A test of relative performance of two covariance matrices is based on work of Diebold and Mariano. The method is applied to stocks and bo…
Dynamic Equicorrelation
A new covariance matrix estimator is proposed under the assumption that at every time period all pairwise correlations are equal. This assumption, which is pragmatically applied in various areas of finance, makes it possible to estimate arbitrarily large covariance matrices with ease. The model, called DECO, involves first adjusting for individual volatilities and then estimating correlations. A quasi-maximum likelihood result shows that DECO pro…
Fitting Vast Dimensional Time-Varying Covariance Models
Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds.…
Econometrics (7 works) · Economics (5 works) · Financial Risk and Volatility Modeling (5 works) · Mathematics (4 works) · Statistics (4 works) · Computer Science (3 works) · Covariance (3 works) · Financial economics (3 works) · Monetary Policy and Economic Impact (3 works) · Portfolio (3 works)