Forecasting commodity futures returns with stepwise regressions: Do commodity-specific factors help?

Massimo Guidolin and Manuela Pedio ()

Annals of Operations Research, 2021, vol. 299, issue 1, No 52, 1317-1356

Abstract: Abstract The aim of this paper is to assess whether three well-known commodity-specific variables (basis, hedging pressure, and momentum) may improve the predictive power for commodity futures returns of models otherwise based on macroeconomic factors. We compute recursive, out-of-sample forecasts for the monthly returns of fifteen commodity futures, when estimation is based on a stepwise model selection approach under a probability-weighted regime-switching regression that identifies different volatility regimes. We systematically compare these forecasts with those produced by a simple AR(1) model that we use as a benchmark and we find that the inclusion of commodity-specific factors does not improve the forecasting power. We perform a back-testing exercise of a mean–variance investment strategy that exploits any predictability of the conditional risk premium of commodities, stocks, and bond returns, also consider transaction costs caused by portfolio rebalancing. The risk-adjusted performance of this strategy does not allow us to conclude that any forecasting approach outperforms the others. However, there is evidence that investment strategies based on commodity-specific predictors outperform the remaining strategies in the high-volatility state.

Keywords: Stepwise regression; Commodity returns; Predictability; Portfolio back-testing (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (11)

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DOI: 10.1007/s10479-020-03515-w

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