Abstract
In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts.
Highlights
Switching Coefficients or AutomaticIn the last few decades, two crucial issues have been identified by the forecasting literature applied to finance
Because recent literature has emphasized that the models yielding the highest statistical accuracy may fail to deliver consistent gains when the forecasts are employed in realistic economic applications, we evaluated the performance of the predictive models under both statistical and economic loss functions
It is worth noting that stepwise regressions have been criticized in the literature because of the widespreadpractice of fitting the final selected model and reporting the estimates without adjusting them to take into account the selection process, or at least appropriately adjusting the outputs related to inference and hypothesis testing
Summary
In the last few decades, two crucial issues have been identified by the forecasting literature applied to finance (see the recent discussion in Akyildirim et al [1]). In any event, when we average the predictive performances over low- vs high-volatility regimes, we find that it remains hard for both HMM and stepwise methods to forecast well, in relative terms These two results do not seem to depend on whether commodity factors are included in the analysis or not, and there is generalized, mild evidence that simpler models including only macroeconomic effects may be “rich enough” to reveal most of the predictability in the data. Allowing flexibility in the choice of the predictors seem to be more relevant than fully characterizing regimes when the objective is to achieve economic gains in the commodity space These results turn out not to depend on the specific coefficient of risk aversion adopted, nor on the fact that the asset menu may include stocks and bonds in addition to commodities.
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