Conditional covariance matrix forecast using the hybrid exponentially weighted moving average approach

This paper extends the hybrid multivariate exponentially weighted moving average (EWMA) approach to incorporate realized variance for conditional covariance matrix forecast. The proposed estimator employs a realized variance‐based EWMA specification to estimate the conditional variance of returns and a standard return‐based EWMA specification to estimate the correlation between each pair of returns. This hybrid realized variance‐based EWMA estimator produces forecasts of the conditional covariance matrix that are statistically more accurate and informative and economically more useful than those produced by the standard EWMA and the multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models using only daily returns. The evidence of incremental forecast accuracy and the economic value over the intraday range‐based hybrid EWMA estimator is, however, insignificant. Moreover, the hybrid EWMA estimators are less sensitive to the choice of decay factors than the standard EWMA model and thus provide a robust framework to accommodate intradaily information for estimates of daily return volatility while achieving a simplicity and ease of implementation.