Journal
ANNALS OF OPERATIONS RESEARCH
Volume 299, Issue 1-2, Pages 1317-1356Publisher
SPRINGER
DOI: 10.1007/s10479-020-03515-w
Keywords
Stepwise regression; Commodity returns; Predictability; Portfolio back-testing
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This paper evaluates the impact of three commodity-specific variables on the predictive power of commodity futures returns, finding that their inclusion does not improve forecasting power. Additionally, the risk-adjusted performance of a mean-variance investment strategy does not show a clear outperformance by any forecasting method.
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.
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