Testing the value of directional forecasts in the presence of serial correlation

Common approaches to testing the economic value of directional forecasts are based on the classical χ2-test for independence, Fisher's exact test or the Pesaran and Timmermann test for market timing. These tests are asymptotically valid for serially independent observations, but in the presence of serial correlation they are markedly oversized, as has been confirmed in a simulation study. We therefore summarize robust test procedures for serial correlation and propose a bootstrap approach, the relative merits of which we illustrate by means of a Monte Carlo study. Our evaluations of directional predictions of stock returns and changes in Euribor rates demonstrate the importance of accounting for serial correlation in economic time series when making such predictions.