This paper introduces a novel generalized autoregressive conditional heteroskedasticity–mixed data sampling–extreme shocks (GARCH‐MIDAS‐ES) model for stock volatility to examine whether the importance of extreme shocks changes in different time ranges. Based on different combinations of the short‐ and long‐term effects caused by extreme events, we extend the standard GARCH‐MIDAS model to characterize the different responses of the stock market for short‐ and long‐term horizons, separately or in combination. The unique timespan of nearly 100 years of the Dow Jones Industrial Average (DJIA) daily returns allows us to understand the stock market volatility under extreme shocks from a historical perspective. The in‐sample empirical results clearly show that the DJIA stock volatility is best fitted to the GARCH‐MIDAS‐SLES model by including the short‐ and long‐term impacts of extreme shocks for all forecasting horizons. The out‐of‐sample results and robustness tests emphasize the significance of decomposing the effect of extreme shocks into short‐ and long‐term effects to improve the accuracy of the DJIA volatility forecasts.

中文翻译：

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在存在极端冲击的情况下预测股票波动性：短期和长期影响

本文介绍了一种新颖的广义自回归条件异方差-混合数据采样-极端震荡（GARCH-MIDAS-ES）模型，用于研究波动率，以检验极端震荡的重要性在不同时间范围内是否发生变化。基于极端事件造成的短期和长期影响的不同组合，我们扩展了标准GARCH-MIDAS模型，以分别或组合地描述了短期和长期视线对股市的不同反应。道琼斯工业平均指数（DJIA）近100年的独特时间跨度使我们能够从历史的角度了解极端冲击下的股市波动。样本中的经验结果清楚地表明，通过将极端震荡的短期和长期影响包括在所有预测范围内，道琼斯工业平均指数的波动性最适合GARCH-MIDAS-SLES模型。样本外结果和稳健性测试强调了将极端冲击的影响分解为短期和长期影响的重要性，以提高DJIA波动率预测的准确性。