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
Hauptverfasser: Guidolin, Massimo, Pedio, Manuela
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2021
Springer
Springer Nature B.V
Schlagworte:
Business and Management
Combinatorics
Commodities
Commodity futures
Economic factors
Forecasting
Forecasts and trends
Futures
Investment strategy
Mathematical models
Methods
Operations research
Operations Research/Decision Theory
Portfolio management
Prices and rates
S.I.: Recent Developments in Financial Modeling and Risk Management
Selection methods (Regression analysis)
Statistical analysis
Supply and demand
Theory of Computation
Volatility
Italy
Portfolio back-testing
Stepwise regression
Predictability
Commodity returns
ISSN:0254-5330, 1572-9338
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-020-03515-w