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.
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Notes
For a numerical example that illustrates how the Mahalanobis distance is used to build a multivariate regression tree, see Behrens et al. (2018).
The data are from Datastream (metal prices) and the FRED board of the Fed of St. Louis (predictors).
The Giacomini and White (2008) test of out-of-sample predictive ability gives similar results (not reported, but available upon request).
Results (not reported) of the Diebold–Mariano test show that multivariate random forests perform significantly better than the VAR models.
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Acknowledgements
We thank two anonymous reviewers for helpful comments. We also thank the German Science Foundation (Deutsche Forschungsgemeinschaft) for financial support (project Macroeconomic Forecasting in Great Crises; Grant No. FR 2677/4-1). The usual disclaimer applies.
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Pierdzioch, C., Risse, M. Forecasting precious metal returns with multivariate random forests. Empir Econ 58, 1167–1184 (2020). https://doi.org/10.1007/s00181-018-1558-9
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DOI: https://doi.org/10.1007/s00181-018-1558-9