Automated Volatility Forecasting
86 Pages Posted: 31 Jan 2021 Last revised: 3 Apr 2024
Date Written: February 28, 2024
Abstract
We develop an automated system to forecast volatility by leveraging over one hundred 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 to 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
JEL Classification: C13, C14, C52, C53, C55, C58
Suggested Citation: Suggested Citation