A machine learning approach to volatility forecasting

Kim Christensen (), Mathias Siggaard () and Bezirgen Veliyev
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Kim Christensen: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Mathias Siggaard: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We show that machine learning (ML) algorithms improve one-day-ahead forecasts of realized variance from 29 Dow Jones Industrial Average index stocks over the sample period 2001 - 2017. We inspect several ML approaches: Regularization, tree-based algorithms, and neural networks. Off-the-shelf ML implementations beat the Heterogeneous AutoRegressive (HAR) model, even when the only predictors employed are the daily, weekly, and monthly lag of realized variance. Moreover, ML algorithms are capable of extracting substantial more information from additional predictors of volatility, including firm-specific characteristics and macroeconomic indicators, relative to an extended HAR model (HAR-X). ML automatically deciphers the often nonlinear relationship among the variables, allowing to identify key associations driving volatility. With accumulated local effect (ALE) plots we show there is a general agreement about the set of the most dominant predictors, but disagreement on their ranking. We investigate the robustness of ML when a large number of irrelevant variables, exhibiting serial correlation and conditional heteroscedasticity, are added to the information set. We document sustained forecasting improvements also in this setting.

Keywords: Gradient boosting; high-frequency data; machine learning; neural network; random forest; realized variance; regularization; volatility forecasting (search for similar items in EconPapers)
JEL-codes: C10 C50 (search for similar items in EconPapers)
Pages: 47
Date: 2021-01-18
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-ore
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Citations: View citations in EconPapers (14)

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Related works:
Journal Article: A Machine Learning Approach to Volatility Forecasting* (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2021-03

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