Forecasting commodity futures returns with stepwise regressions: Do commodity-specific factors help?
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 mo...
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| Veröffentlicht in: | Annals of operations research Jg. 299; H. 1-2; S. 1317 - 1356 |
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| 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Guidolin, Massimo Pedio, Manuela |
| Author_xml | – sequence: 1 givenname: Massimo surname: Guidolin fullname: Guidolin, Massimo organization: Finance Department, Bocconi University and Baffi-CAREFIN Centre – sequence: 2 givenname: Manuela surname: Pedio fullname: Pedio, Manuela email: Manuela.pedio@unibocconi.it organization: Finance Department, Bocconi University and Baffi-CAREFIN Centre |
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| Keywords | Portfolio back-testing Stepwise regression Predictability Commodity returns |
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| Title | Forecasting commodity futures returns with stepwise regressions: Do commodity-specific factors help? |
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