Automated Volatility Forecasting

75 Pages Posted: 31 Jan 2021 Last revised: 20 May 2023

See all articles by Sophia Zhengzi Li

Sophia Zhengzi Li

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick

Yushan Tang

Nankai Business School, Nankai University

Date Written: May 19, 2023

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 an enhanced annual return around 8.5% 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

Li, Sophia Zhengzi and Tang, Yushan, Automated Volatility Forecasting (May 19, 2023). Available at SSRN: https://ssrn.com/abstract=3776915 or http://dx.doi.org/10.2139/ssrn.3776915

Sophia Zhengzi Li (Contact Author)

Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick ( email )

100 Rockafeller Rd
Piscataway, NJ 08854
United States

HOME PAGE: http://https://sites.google.com/site/szlwebpage/

Yushan Tang

Nankai Business School, Nankai University ( email )

Balitai, 94 weijing Rd
Tianjin, 300071
China

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