Machine Learning and the Forecastability of Cross-Sectional Realized Variance: The Role of Realized Moments

Vasilios Plakandaras (), Matteo Bonato (), Rangan Gupta () and Oguzhan Cepni
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Vasilios Plakandaras: Department of Economics, Democritus University of Thrace, Komotini, Greece
Matteo Bonato: Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France
Rangan Gupta: Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa

No 202518, Working Papers from University of Pretoria, Department of Economics

Abstract: This paper forecasts monthly cross-sectional realized variance (RV) for U.S. equities across 49 industries and all 50 states. We exploit information in both own-market and cross-market (oil) realized moments (semi-variance, leverage, skewness, kurtosis, and upside and downside tail risk) as predictors. To accommodate cross-sectional dependence, we compare standard econometric panel models with machine-learning approaches and introduce a new machine-learning technique tailored specifically to panel data. Using observations from April 1994 through April 2023, the panel-dedicated machine-learning model consistently outperforms all other methods, while oil-related moments add little incremental predictive power beyond own-market moments. Short-horizon forecasts successfully capture immediate shocks, whereas longer-horizon forecasts reflect broader structural economic changes. These results carry important implications for portfolio allocation and risk management.

Keywords: Cross-sectional realized variance; Realized moments; Machine learning; Forecasting (search for similar items in EconPapers)
JEL-codes: C33 C53 G10 G17 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2025-04
New Economics Papers: this item is included in nep-fmk and nep-for
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