The paper “Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach” by Dr. Qiao Yang has been accepted for publication at Econometrics, MDPI.
Existing methods of evaluating Realized Covariance (RCOV) estimators empirically rely on portfolio analysis, which compare RCOV measures indirectly using univariate measures such as portfolio standard deviation or Sharpe ratio. The comparison of RCOV measures’ predictive power on return density forecasts is not well investigated in the existing literature. This paper fills the gap by suggesting a density-forecast-based method to evaluate RCOV measures. Given that covariances are not observable, while returns are, the joint modeling of returns and RCOVs enables the evaluation of RCOV estimators via return density forecasts. We test the empirical predictive power of a list of popular RCOV estimators and found several estimators consistently outperform others. The density-forecast-based evaluation method is robust to various RCOV models, datasets, data dimensions and forecast horizons. Another important insight is that the RCOV measures should be carefully selected in covariance modeling, as the choice of RCOV measures can significantly impact a model’s forecasting performance.