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Forecasting precious metal returns with multivariate random forests

Author

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  • Christian Pierdzioch

    (Helmut Schmidt University)

  • Marian Risse

    (Helmut Schmidt University)

Abstract

We use multivariate random forests to compute out-of-sample forecasts of a vector of returns of four precious metal prices (gold, silver, platinum, and palladium). We compare the multivariate forecasts with univariate out-of-sample forecasts implied by random forests independently fitted to every single return series. Using univariate and multivariate forecast evaluation criteria, we show that multivariate forecasts are more accurate than univariate forecasts.

Suggested Citation

  • Christian Pierdzioch & Marian Risse, 2020. "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, vol. 58(3), pages 1167-1184, March.
    • Handle: RePEc:spr:empeco:v:58:y:2020:i:3:d:10.1007_s00181-018-1558-9
    DOI: 10.1007/s00181-018-1558-9
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    Cited by:

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    2. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    3. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2024. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 487-513, July.
    4. David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
    5. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
    6. Martha Carpinteyro & Francisco Venegas-Martínez & Alí Aali-Bujari, 2021. "Modeling Precious Metal Returns through Fractional Jump-Diffusion Processes Combined with Markov Regime-Switching Stochastic Volatility," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    7. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
    8. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    9. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).

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    More about this item

    Keywords

    Precious metals; Forecasting; Random forests;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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