Published and forthcoming articles

[Link to published version]  [Link to SSRN version]

We study the out-of-sample predictability of monthly crude oil prices using forecast combinations constructed from several individual predictors. Our empirical results indicate that combination forecasts monthly average oil prices are more accurate than the no-change forecast with statistically significant reductions in mean square forecast errors (MSFE) and significant directional accuracy at every horizon up to 24 months, consistent with earlier evidence that forecast combinations greatly enhance the forecastability of oil prices. In contrast, we find no significant MSFE reductions or directional accuracy for forecasts of end-of-month oil prices at almost all horizons. Furthermore, we document that end-of-month forecasts when used to guide investment and hedging decisions of investors, statistically, do not deliver superior economic value to investors. Overall, the implication of our results is that the statistical and economic significance of forecasts of oil prices is heavily influenced by the construction of the underlying oil price series and provide a cautionary note on which oil price series to use in forecasting. 


[Link to published version]

We find out-of-sample predictability of commodity futures excess returns using forecast combinations of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean-variance investor. Commodity return forecasts are closely linked to the real economy. Return predictability is countercyclical, and the combination forecasts of commodity returns have significantly positive predictive power for future economic activity. Two-factor models featuring innovations in each of the combination forecasts and the market factor explain a substantial proportion of the cross-sectional variation of commodity and equity returns. The associated positive risk prices are consistent with the Intertemporal Capital Asset Pricing Model (ICAPM), given how the predictors forecast an increase in future economic activity in the time-series. Overall, combination forecasts act as state variables within the ICAPM, thus resurrecting a central role for macroeconomic risk in determining expected returns on commodities.

[Link to published version]  [Link to SSRN version] 

Previous studies document statistically significant evidence of crude oil return predictability by several forecasting variables. We suggest that this evidence is misleading, and follows from the common use of within-month averages of daily oil price data in return predictive regressions. Averaging introduces a bias in the estimates of the first-order autocorrelation coefficient and variance of returns. Consequently, estimates of regression coefficients are inefficient and associated t-statistics are overstated, leading to false inference about the true extent of return predictability. On the contrary, using end-of-month data, we do not find convincing evidence for the predictability of oil returns. Our results highlight and provide a cautionary tale on how the choice of data could influence hypothesis testing for return predictability.

[Link to published version]

We re-examine diversification benefits of investing in commodities and currencies by considering a risk-averse investor with mean-variance preferences who exploits the possibility of predictable time variation in asset return means, variances, and covariances. We implement unconditional and conditional efficient portfolio strategies designed to exploit this predictability, together with more traditional and/or ad hoc ones yet hitherto relatively unexplored in this context (including the equally weighted, fixed weight, volatility timing, and reward-to-risk timing strategies). We find that, for all portfolio strategies, commodities and currencies do not improve the investment opportunity set of the investor with an existing portfolio of stocks, bonds and T-bills, and an investment horizon of one month. Our findings, which reverse the conclusions of previous studies that focus on static portfolio strategies, are robust across several performance metrics.