Conditional Evaluation of Exchange Rate Predictive Ability in Long Run Regressions

In this paper we evaluate exchange rate predictability using a new framework developed by Giacomini and White (2004). In this new framework we test for conditional predictive ability rather than for unconditional predictive ability, which has been the usual approach thus far. 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 best 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.