Testing the predictability of commodity prices in stock returns of G7 countries: Evidence from a new approach

• Published in: Resources policy Vol. 64; p. 101520
• Main Authors: Salisu, Afees A., Isah, Kazeem O., Raheem, Ibrahim D.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2019; Elsevier Science Ltd
• Subjects: Alternative approaches; Bias; Commodities; Commodity prices; Economic models; G7 countries; Mathematical models; Power; Predictive model; Prices; Pricing; Stock returns; Time series; G7 countries; C52; E44; Predictive model; Stock returns; G17; Commodity prices
• ISSN: 0301-4207; 1873-7641
• DOI: 10.1016/j.resourpol.2019.101520

In this paper, we offer an alternative approach to test the predictive power of commodity prices in stock returns of G7 countries. The new approach accounts for asymmetry, conditional heteroscedasticity, endogeneity, persistence, and structural breaks that may bias the forecast outcomes. Three striking findings are highlighted from the various analyses. First, commodity prices are good predictors of stock returns both for in-sample and out-of-sample forecasts. Second, the proposed commodity-based model for stock returns outperforms both the time series models as well as historical average models that ignore same. Third, these conclusions are robust to different components of commodity prices, multiple data samples and alternative forecast horizons. •Variants of commodity-based predictive models for stock returns are formulated. .•Models that account for salient features of predictors have better forecast results.•Accounting for significant breaks improves the predictability of commodity prices.•Commodity-based predictive models outperform historical average.•Findings are robust to multiple data samples & different forecast horizons.