Long Memory and Asymmetry for Matrix-Exponential Dynamic Correlation Processes

We propose a fractionally integrated matrix-exponential dynamic conditional correlation (FIEDCC) model to capture the asymmetric effects and long- and short-range dependence of a correlation process. We also propose employing an inverse Wishart distribution for the disturbance of a covariance structure, which gives an alternative interpretation for a multivariate t conditional distribution. Using the inverse Wishart distribution, we present a three-step procedure to obtain initial values for estimating a high-dimensional conditional covariance model with a multivariate t distribution. We investigated the finite-sample properties of the ML estimator. Empirical results for nine assets from chemical firms, banks, and oil and gas producers in the US indicate that the new FIEDCC model outperforms the other dynamic correlation models for the AIC and BIC and for forecasting value-at-risk thresholds. Furthermore, the new FIEDCC model captures the stronger connection among the nine assets for the period right after the global financial crisis.