Forecasting exchange rates better than the random walk thanks to machine learning techniques
Résumé
Simple exchange rate models based on economic fundamentals were shown to have a difficulty in beating the random walk when predicting the exchange rates out of sample in the modern floating era. Using methods from machine learning -- adaptive sequential ridge regression with discount factors -- that prevent overfitting in-sample for better and more stable forecasting performance out-of-sample we show that fundamentals from the PPP, UIRP and monetary models consistently improve the accuracy of exchange rate forecasts for major currencies over the floating period era 1973--2013 and are able to beat the random walk prediction giving up to 5% improvements in terms of the RMSE at a 1 month forecast. ''Classic'' fundamentals hence contain useful information about exchange rates even for short forecasting horizons - and the Meese and Rogoff [1983] puzzle is overturned. Such conclusions cannot be obtained when rolling or recursive OLS regressions are used as is common in the literature.
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