Temporal Aggregation and the Estimation of Reverse Regressions for Commodities Market Models

The main purpose of this article is to empirically demonstrate the effects of temporal aggregation when applying reverse regression models hypothesizing that spot prices today help predict forward rates in the future. This paper essentially reviews results from earlier research indicating that time-series aggregation will most certainly influence standard errors on parameter estimates. Standard errors are likely to increase with aggregation. The relationships between futures prices and spot oil prices are analyzed along with the importance of the effects of temporal aggregation and alternative model specification for understanding empirical relationships between the markets. Model specification and time-series aggregation over daily, weekly, and monthly aggregations confirm evidence that estimated standard errors are likely to increase with aggregation and t-ratios change as well. While goodness-of-fit measures might increase with aggregation, forecast accuracy with macrolevel aggregation might deteriorate owing to information loss due to the averaging of observations associated with underlying microstructures.