Subsample results.

This paper examines the linkage between Chinese stock market volatility and investor attention fluctuation. In Heterogeneous autoregressive (HAR) model, first, we analyzed the linkage between both decomposed and undecomposed stock market realized volatility and investor attention fluctuations across full-sample and two-year moving window sub-samples. Second, we compare the predictive power of four models in short-, medium-, and long-term volatility forecasting. Empirical results show large positive attention fluctuation amplified Chinese stock market volatility after the outbreak of COVID-19, and negative small attention fluctuation significantly stabilized stock market volatility before COVID-19, and the impact dwindled in after COVID-19. The model incorporating decomposed realized volatility and decomposed attention fluctuation performs better in volatility Forecasting. This research underscores a shift in the dynamics between stock market volatility and investor attention fluctuations, and investor attention fluctuation improves the volatility forecasting accuracy of the Chinese stock market.

本文探讨了中国股票市场波动率与投资者注意力波动之间的关联。首先，基于异质自回归（Heterogeneous autoregressive, HAR）模型，我们分别在全样本区间与两年滚动窗口子样本区间下，分析了经分解与未分解的股票市场已实现波动率（realized volatility）与投资者注意力波动之间的关联。其次，我们对比了四类模型在短期、中期与长期波动率预测中的预测性能。实证结果表明：新冠疫情爆发后，大幅正向的注意力波动会加剧中国股票市场的波动率；而疫情前，小幅负向的注意力波动则会显著平抑市场波动率，且该影响在疫情后有所减弱。整合了经分解的已实现波动率与经分解的注意力波动的模型，在波动率预测任务中表现更优。本研究揭示了股票市场波动率与投资者注意力波动之间的动态关系已发生转变，且投资者注意力波动能够有效提升中国股票市场波动率的预测精度。