Testing the predictability of commodity prices in stock returns: A new perspective

In this paper, we offer an alternative approach for testing the predictive power of commodity prices in stock returns using monthly data of about six decades. In the process, we account for prominent features of predictive models such as asymmetry, conditional heteroscedasticity, endogeneity, persistence, and structural breaks that may bias the forecast outcomes. Using the G7 stock exchanges, three findings are discernible 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 that accounts for the highlighted features outperforms both the traditional predictive model as well historical average models that ignore same. Thirdly, these conclusions are robust to different components of commodity prices, multiple data samples and alternative forecast horizons.