Dynamic Conditional Correlation
Robert F. EngleDepartment of Finance, New York University, Leonard N. Stern School of Business, New York, NY 10012, and Department of Economics, University of California, San Diego
2002en
ABI
Abstract
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 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 provide sensible empirical results.
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