Exploiting Intraday Decompositions in Realized Volatility Forecasting: A Forecast Reconciliation Approach
研究了如何利用已实现方差的层级结构来改进预测，提出一种事后协调方法，通过对道琼斯工业平均指数及其成分股的数据验证，发现利用层级信息能提升预测精度。
Abstract We address the construction of Realized Variance (RV) forecasts by exploiting the hierarchical structure implicit in available decompositions of RV. We propose a post-forecasting approach that utilizes bottom-up and regression-based reconciliation methods. 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.