Conditional Predictive Ability of Exchange Rates in Long Run Regressions

In this paper we evaluate exchange rate predictability using a framework developed by Giacomini and White (2006). This new framework tests for conditional predictive ability rather than unconditional predictive ability, which has been the standard approach. Using several shrinkage based forecasting methods, including new methods proposed here, we evaluate conditional predictability of five bilateral exchange rates at differing horizons. Our results indicate that for most currencies a random walk would not be the optimal forecasting method in a real time forecasting exercise, at least for some predictive horizons. We also show that our proposed shrinkage methods in general perform on par with Bayesian shrinkage and ridge regressions, and sometimes they even perform better.