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Automated Volatility Forecasting

Author

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  • Sophia Zhengzi Li

    (Rutgers Business School, Newark, New Jersey 07102)

  • Yushan Tang

    (Dishui Lake Advanced Finance Institute, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

We develop an automated system to forecast volatility by leveraging more than 100 features and five machine learning algorithms. Considering the universe of S&P 100 stocks, our system results in superior out-of-sample volatility forecasts compared with existing risk models across forecast horizons. We further demonstrate that our system remains robust to different specifications and is scalable to a broader S&P 500 stock universe via hyperparameter transfer learning. Finally, the statistical improvement in volatility forecasts translates into significant annual returns from a cross-sectional variance risk premium strategy.

Suggested Citation

  • Sophia Zhengzi Li & Yushan Tang, 2025. "Automated Volatility Forecasting," Management Science, INFORMS, vol. 71(7), pages 6248-6274, July.
    • Handle: RePEc:inm:ormnsc:v:71:y:2025:i:7:p:6248-6274
    DOI: 10.1287/mnsc.2023.01520
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