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Forecasting precious metal returns with multivariate random forests

Author

Listed:
  • Christian Pierdzioch

    () (Helmut Schmidt University)

  • Marian Risse

    (Helmut Schmidt University)

Abstract

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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    References listed on IDEAS

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    More about this item

    Keywords

    Precious metals; Forecasting; Random forests;

    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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