Automated Volatility Forecasting

Sophia Zhengzi Li () and Yushan Tang ()
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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

Management Science, 2025, vol. 71, issue 7, 6248-6274

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.

Keywords: automation; machine learning; volatility forecasting; high-frequency data; transfer learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:7:p:6248-6274

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