Dynamic Equicorrelation

Abstract

A new covariance matrix estimator is proposed under the assumption that at every time period all pairwise correlations are equal. This assumption, which is pragmati- cally applied in various areas of finance, makes it possible to estimate arbitrarily large covariance matrices with ease. The model, called DECO, is a special case of the CCC and DCC models which involve first adjusting for individual volatilities and then estimating the correlations. A QMLE result shows that DECO can continue to give consistent parameter estimates when the equicorrelation assumption is violated. Generalizations to block equicorrelation structures, models with exogenous variables, and alternative specifications are explored and diagnostic tests are proposed. Estimation is evaluated by Monte Carlo and using US stock return data.