Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance

摘要

Modelling covariance structures is known to suffer from the curse ofdimensionality. In order to avoid this problem for forecasting, the authors propose a newfactor multivariate stochastic volatility (fMSV) model for realized covariance measures thataccommodates asymmetry and long memory. Using the basic structure of the fMSV model,the authors extend the dynamic correlation MSV model, the conditional/stochastic Wishartautoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model.Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSVmodels outperform existing dynamic conditional correlation models for forecasting futurecovariances. Among the new fMSV models, the Cholesky MSV model with long memoryand asymmetry shows stable and better forecasting performance for one-day, five-day andten-day horizons in the periods before, during and after the global financial crisis.