Revealing Exchange Rate Fundamentals by Bootstrap

Abstract

Research shows that the predictive ability of economic fundamentals for exchange rates is time-varying; it may be detected in some periods and disappear in others. This paper uses bootstrap-based methods to uncover the time-specific conditioning information for predicting exchange rates. Employing measures of predictive ability over time, statistical and economic evaluation criteria, we find that our approach based on pre-selecting and validating fundamentals across bootstrap replications leads to significant forecasts improvements and economic gains. The approach, known as bumping, robustly reveals parsimonious models with out-of-sample predictive power at 1-month horizon; and outperforms alternative methods, including Bayesian, bagging, and standard forecast combinations.