Abstract
This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast performance of a covariance estimator can be assessed according to its improvement in return density forecasting. Empirical applications to equity data show that several RCOV estimators consistently perform better than others and emphasize the importance of RCOV selection in covariance modeling and forecasting.
Highlights
The past two decades have seen dramatic growth in the amount of literature on estimating and modeling realized covariance (RCOV) measures
This paper suggests a joint return and RCOV modeling approach to assess RCOV measures based on return density forecasts
This direction of research contributes to the ex-post covariance estimation literature by proposing a new evaluation method and to the RCOV modeling literature as we show that the choice of estimator matters to a model’s predictability
Summary
The past two decades have seen dramatic growth in the amount of literature on estimating and modeling realized covariance (RCOV) measures. This paper suggests a joint return and RCOV modeling approach to assess RCOV measures based on return density forecasts. This direction of research contributes to the ex-post covariance estimation literature by proposing a new evaluation method and to the RCOV modeling literature as we show that the choice of estimator matters to a model’s predictability. Aït-Sahalia and Mancini (2008) rely on simulated data to compare out-of-sample forecasts of two realized volatility (RV) estimators. This paper suggests an approach based on return density forecasts to evaluate RCOV estimators. The density-forecast-based approach offers a direct and improved way to evaluate the out-of-sample performance of RCOV measures.
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