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.

中文翻译：

---

使用混合指数加权移动平均法的条件协方差矩阵预测

本文扩展了混合多元指数加权移动平均 (EWMA) 方法，以纳入条件协方差矩阵预测的已实现方差。建议的估计器采用基于已实现方差的 EWMA 规范来估计回报的条件方差，并采用基于标准回报的 EWMA 规范来估计每对回报之间的相关性。这种基于混合实现方差的 EWMA 估计器产生的条件协方差矩阵的预测比标准 EWMA 和仅使用每日收益的多元广义自回归条件异方差 (GARCH) 模型产生的预测在统计上更准确、信息更丰富、在经济上更有用。然而，基于日内范围的混合 EWMA 估计器的增量预测准确性和经济价值的证据是微不足道的。此外，与标准 EWMA 模型相比，混合 EWMA 估计器对衰减因子的选择不那么敏感，因此提供了一个强大的框架来适应日内信息以估计每日回报波动率，同时实现简单和易于实施。