We address the construction of Realized Variance (RV) forecasts by exploiting the hierarchical structure implicit in available decompositions of RV. By using data referred to the Dow Jones Industrial Average Index and to its constituents we show that exploiting the informative content of hierarchies improves the forecast accuracy. Forecasting performance is evaluated out-of-sample based on the empirical MSE and QLIKE criteria as well as using the Model Confidence Set approach.

翻译：我们通过利用已实现方差（RV）内在分解中隐含的层级结构，构建其预测方法。基于道琼斯工业平均指数及其成分股的数据，我们证明利用层级结构的信息内容能够提升预测精度。通过基于实证MSE和QLIKE准则的样本外预测性能评估，并结合模型置信集方法，我们对预测效果进行了验证。