Automated Volatility Forecasting

• 成果类型: Article
• 署名作者: Li, Sophia Zhengzi; Tang, Yushan
• 署名单位: Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; Shanghai University of Finance & Economics
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.01520
• 发表日期: 2025
• 页码: 6248-6274
• 关键词: automation; Machine Learning; Volatility forecasting; high-frequency data; Transfer Learning

摘要

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.