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

Author

Listed:
  • 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)

  • Oguzhan Cepni

    (Ostim Technical University, Ankara, Turkiye; University of Edinburgh Business School, Centre for Business, Climate Change, and Sustainability; Department of Economics, Copenhagen Business School, Denmark)

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.

Suggested Citation

  • Vasilios Plakandaras & Matteo Bonato & Rangan Gupta & Oguzhan Cepni, 2025. "Machine Learning and the Forecastability of Cross-Sectional Realized Variance: The Role of Realized Moments," Working Papers 202518, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202518
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    References listed on IDEAS

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    1. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2088-2125, September.
    2. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2022. "Forecasting realized volatility of international REITs: The role of realized skewness and realized kurtosis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 303-315, March.
    3. Angelidis, Timotheos & Sakkas, Athanasios & Tessaromatis, Nikolaos, 2015. "Stock market dispersion, the business cycle and expected factor returns," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 265-279.
    4. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    5. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    6. Byun, Sung Je, 2016. "The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 162-180.
    7. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna, 2022. "A moving average heterogeneous autoregressive model for forecasting the realized volatility of the US stock market: Evidence from over a century of data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 384-400, January.
    8. Amaya, Diego & Christoffersen, Peter & Jacobs, Kris & Vasquez, Aurelio, 2015. "Does realized skewness predict the cross-section of equity returns?," Journal of Financial Economics, Elsevier, vol. 118(1), pages 135-167.
    9. Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 111-122, January.
    10. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    11. Rangan Gupta & Christian Pierdzioch, 2024. "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Mathematics, MDPI, vol. 12(18), pages 1-26, September.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Riza Demirer & Rangan Gupta & Zhihui Lv & Wing-Keung Wong, 2019. "Equity Return Dispersion and Stock Market Volatility: Evidence from Multivariate Linear and Nonlinear Causality Tests," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    14. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    15. Vasilios Plakandaras & Ioannis Pragidis & Paris Karypidis, 2024. "Deciphering the U.S. metropolitan house price dynamics," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 52(2), pages 434-485, March.
    16. Soosung Hwang & Steve Satchell, 2005. "GARCH model with cross-sectional volatility: GARCHX models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(3), pages 203-216.
    17. Hong, Harrison & Torous, Walter & Valkanov, Rossen, 2007. "Do industries lead stock markets?," Journal of Financial Economics, Elsevier, vol. 83(2), pages 367-396, February.
    18. Lu, Fei & Ma, Feng, 2023. "Cross-sectional uncertainty and stock market volatility: New evidence," Finance Research Letters, Elsevier, vol. 57(C).
    19. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    20. Niu, Zibo & Demirer, Riza & Suleman, Muhammad Tahir & Zhang, Hongwei & Zhu, Xuehong, 2024. "Do industries predict stock market volatility? Evidence from machine learning models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    21. Souropanis, Ioannis & Vivian, Andrew, 2023. "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, vol. 74(C).
    22. Chikashi Tsuji, 2012. "Do industries contain predictive information for the Fama--French factors?," Quantitative Finance, Taylor & Francis Journals, vol. 12(6), pages 969-991, March.
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    More about this item

    Keywords

    Cross-sectional realized variance; Realized moments; Machine learning; Forecasting;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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