Volatility Forecasting—Machine Learning or Econometric Model?

Abstract

This paper utilises high‐frequency data from the Chinese stock market and a panel of individual stocks to compare the forecasting performance of machine learning with popular econometric models across different periods. For short‐term volatility forecasting, most machine learning models outperform econometric models with limited explanatory variables, though they do not exhibit a significant advantage over the econometric model incorporating all features. For medium‐ and long‐term volatility forecasting, Light gradient boosting machine (LGBM) in machine learning substantially dominates econometric models. We also explore a simple‐to‐implement forecast combination method that leverages the best machine learning model and the best econometric model to explore if model averaging leads to any improvement. Our findings indicate that over a longer forecasting horizon, this method achieves the best performance among all forecast combinations and dominates the best econometric model.