Forecasting precious metal returns with multivariate random forests

Christian Pierdzioch and Marian Risse

Empirical Economics, 2020, vol. 58, issue 3, No 11, 1167-1184

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.

Keywords: Precious metals; Forecasting; Random forests (search for similar items in EconPapers)
JEL-codes: C53 G17 Q02 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s00181-018-1558-9

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