Machine Learning-Based Implied Volatility Prediction and Trading Models for Options

The occurrence of a series of major emergencies, such as the Russian-Ukrainian conflict and the COVID-19 epidemic, has caused a huge impact on the financial market, and the volatility of the global market has increased significantly. Facing the volatility risk brought by market uncertainty shocks, accurate prediction of volatility is critical to maintaining stability in financial markets. This paper uses 39 factors and 5 machine learning algorithms to build prediction models for the direction of the option implied volatility. Using the SSE 50ETF option data, this paper examine the out-of-sample performance of different models from two perspectives: statistical prediction accuracy and economic gain of volatility strategy. Compared with Logit model, nonlinear models such as random forest have better performance in predicting the direction of implied volatility, and it is helpful to improve the prediction accuracy by combining multiple models. We rely on the prediction signal to trade delta-neutral option straddle, and find that the random forest model can obtain the highest out-of-sample Sharp ratio. The results of this paper suggest that the use of nonlinear machine learning models helps to improve the forecasting accuracy of implied volatility and improves the out-of-sample performance of volatility trading strategies, which helps investors better manage volatility risk or conduct volatility trading, which is an important revelation for asset pricing and risk management.