40 Pages Posted: 9 Mar 2009 Last revised: 10 Nov 2015
Date Written: June 1, 2011
A new covariance matrix estimator is proposed under the assumption that at every time period all pairwise correlations are equal. This assumption, which is pragmatically applied in various areas of finance, makes it possible to estimate arbitrarily large covariance matrices with ease. The model, called DECO, involves first adjusting for individual volatilities and then estimating correlations. A quasi-maximum likelihood result shows that DECO provides consistent parameter estimates even when the equicorrelation assumption is violated. We demonstrate how to generalize DECO to block equicorrelation structures. DECO estimates for US stock return data show that (block) equicorrelated models can provide a better fit of the data than DCC. Using out-of-sample forecasts, DECO and Block DECO are shown to improve portfolio selection compared to an unrestricted dynamic correlation structure.
Suggested Citation: Suggested Citation
Engle, Robert F. and Kelly, Bryan T., Dynamic Equicorrelation (June 1, 2011). NYU Working Paper No. FIN-08-038; Chicago Booth Research Paper No. 12-07; Fama-Miller Working Paper. Available at SSRN: https://ssrn.com/abstract=1354525 or http://dx.doi.org/10.2139/ssrn.1354525
By Andrew Ang