Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models

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Dynamic Conditional Correlation - A Simple Class of Multivariate GARCH Models

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Robert F. Engle

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Robert F. Engle

Abstract

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of returns. A new class of multivariate models called dynamic conditional correlation (DCC) 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 estimated very simply with univariate or two step methods based on the likelihood function. It is shown that they perform well in a variety of situations and give sensible empirical results.

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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 2000-09.

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Date of creation: May 2000
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Handle: RePEc:cdl:ucsdec:2000-09

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