Dynamic Conditional Correlation : A Simple Class of Multivariate GARCH Models
Robert F. Engle
New York University - Leonard N. Stern School of Business - Department of Economics; Centre for International Finance and Regulation (CIFR); National Bureau of Economic Research (NBER); New York University (NYU) - Department of Finance
NYU Working Paper No. S-DRP-02-01
Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. 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 andprovide sensible empirical results.
Number of Pages in PDF File: 34working papers series
Date posted: November 7, 2008
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