Predicting Stock Jumps and Crashes Using Options
研究期权隐含波动率和希腊字母能否预测股票极端涨跌，发现期权变量（尤其是隐含波动率和Delta）比股票特征更有效，且看跌期权对崩盘的预测力更强。
ABSTRACT This paper investigates the informativeness of option‐implied volatility and Greeks in forecasting extreme stock returns. Using a large data set of U.S. stocks and options from 1996 to 2022 and employing Light Gradient‐Boosting Machine as a machine learning algorithm, we show that option characteristics, particularly implied volatility and delta, are strong predictors of extreme returns. The long–short portfolio utilizing option variables significantly outperforms a benchmark using only stock characteristics, suggesting that options provide information beyond what can be inferred from stock characteristics. Put options are revealed to be more informative than call options, and crashes are easier to predict than jumps.