Fitting and Testing Vast Dimensional Time-Varying Covariance Models

28 Pages Posted: 3 Nov 2008

See all articles by Robert F. Engle

Robert F. Engle

New York University - Leonard N. Stern School of Business - Department of Economics; New York University (NYU) - Department of Finance; National Bureau of Economic Research (NBER)

Neil Shephard

Harvard University

Kevin Sheppard

University of Oxford - Department of Economics; University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: October 2007

Abstract

Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel way of estimating models of time-varying covariances that overcome some of the computational problems which have troubled existing methods when applied to 1,000s of assets. The theory of this new strategy is developed in some detail, allowingformal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method.

Suggested Citation

Engle, Robert F. and Shephard, Neil and Sheppard, Kevin Keith, Fitting and Testing Vast Dimensional Time-Varying Covariance Models (October 2007). NYU Working Paper No. FIN-07-046. Available at SSRN: https://ssrn.com/abstract=1293629

Robert F. Engle (Contact Author)

New York University - Leonard N. Stern School of Business - Department of Economics ( email )

269 Mercer Street
New York, NY 10003
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New York University (NYU) - Department of Finance

Stern School of Business
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United States

National Bureau of Economic Research (NBER)

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Neil Shephard

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Kevin Keith Sheppard

University of Oxford - Department of Economics ( email )

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Oxford, OX1 3BJ
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

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HOME PAGE: http://www.oxford-man.ox.ac.uk

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