Historical Perspectives in Volatility Forecasting Methods with Machine Learning

Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo and Eun Sang Cha ()
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Zhiang Qiu: Seaver College, Pepperdine University, Malibu, CA 90263, USA
Clemens Kownatzki: Pepperdine Graziadio Business School, Pepperdine University, Malibu, CA 90263, USA
Fabien Scalzo: Seaver College, Pepperdine University, Malibu, CA 90263, USA
Eun Sang Cha: Seaver College, Pepperdine University, Malibu, CA 90263, USA

Risks, 2025, vol. 13, issue 5, 1-24

Abstract: Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to further research in learning-based methods for volatility forecasting.

Keywords: volatility forecasting; risk management; deep learning; time series analysis; GARCH; LSTM; transformer (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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