Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach

In this study, we propose a new family of the heterogeneous autoregressive realized volatility (HAR‐RV) models by considering truncated methods for predicting the RV in China's stock market. By adopting three types of critical values to recognize extremely large values of RV, we show that the modified models are simple but efficient to consistently deliver stronger in‐sample and out‐of‐sample forecasting performances than those of existing methods. Models that take truncated approaches into account can generate substantial economic gains in applications. We further provide evidence that the superiority of our proposed models is derived from the reduced variance of the measurement errors during days including truncated RVs. Additionally, the improved performances of the modified models still hold after considering the effects of jump components and leverage, as well as a wide range of extensions and robustness analyses.