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Large dynamic covariance matrices


Engle, Robert F; Ledoit, Olivier; Wolf, Michael (2019). Large dynamic covariance matrices. Journal of Business and Economic Statistics, 37(2):363-375.

Abstract

Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroscedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In the cross-section, the key is to correct in-sample biases of sample covariance matrix eigenvalues; a favored model is nonlinear shrinkage, derived from Random Matrix Theory (RMT). The present article marries these two strands of literature to deliver improved estimation of large dynamic covariance matrices. Supplementary material for this article is available online.

Abstract

Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroscedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In the cross-section, the key is to correct in-sample biases of sample covariance matrix eigenvalues; a favored model is nonlinear shrinkage, derived from Random Matrix Theory (RMT). The present article marries these two strands of literature to deliver improved estimation of large dynamic covariance matrices. Supplementary material for this article is available online.

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Composite likelihood, dynamic conditional correlation, GARCH, Markowitz portfolio selection, nonlinear shrinkage, statistics, probability and uncertainty, economics and econometrics, statistics and probability, social sciences (miscellaneous)
Language:English
Date:3 April 2019
Deposited On:15 May 2019 11:20
Last Modified:15 May 2019 11:20
Publisher:American Statistical Association
ISSN:0735-0015
OA Status:Green
Publisher DOI:https://doi.org/10.1080/07350015.2017.1345683
Official URL:https://www.tandfonline.com/doi/full/10.1080/07350015.2017.1345683

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