Forecasting stock volatility is of great interest to academics and practitioners because volatility has important implications for many areas such as risk management and portfolio allocation. Recent studies show that economic variables fail to predict stock volatility beyond lagged volatility. In this paper, we find that realized skewness shows significant predictive ability for future realized volatility. We use the daily price data of the S&P 500 index over a long sample period spanning 1928 to 2019 to construct skewness predictors, and reveal the negative relationship between realized skewness and volatility. The realized skewness significantly outperforms the benchmark of the autoregressive model in short horizons and contains different predictive information from macroeconomic indicators and volatility of volatility. The predictive ability of skewness is also found in most industry portfolios. The realized skewness predicts volatility mainly through risk transmission channel, and then through the business cycle channel.

预测股票波动性对学术界和从业者非常感兴趣，因为波动性对风险管理和投资组合分配等许多领域具有重要意义。最近的研究表明，除了滞后波动之外，经济变量无法预测股票波动。在本文中，我们发现已实现偏度对未来已实现波动率显示出显着的预测能力。我们使用标准普尔 500 指数在 1928 年至 2019 年的长期样本期间的每日价格数据来构建偏度预测变量，并揭示已实现偏度与波动率之间的负相关关系。已实现偏度在短期内显着优于自回归模型的基准，并且包含来自宏观经济指标和波动率波动率的不同预测信息。在大多数行业投资组合中也可以找到偏度的预测能力。已实现偏度主要通过风险传导渠道预测波动性，然后通过商业周期渠道。