Is a random walk the best exchange rate predictor?

The paper discusses short-term exchange rate prediction, using the random walk hypothesis (RWH) as a benchmark to compare performances. After surveying some recent results in this field, the authors suggest a filtering–prediction method inspired by recent developments in nonlinear dynamical systems theory. The filtering of presumably noisy data is realized by means of a technique derived from Singular Spectrum Analysis (SSA) conveniently adapted to a nonlinear dynamics context. In particular, the authors develop a multichannel version of SSA. Filtered data are then used to perform an out-of-sample, short-term prediction, by means of a nonlinear (locally linear) method. This method is applied to exchange rate series of the major currencies and the predictions thus obtained are shown to outperform neatly those derived from the RWH. Finally, the application of a test recently developed by Mizrach confirms the statistical significance of the results.