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Forecasting exchange rates better than the random walk thanks to machine learning techniques

Abstract : 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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https://halshs.archives-ouvertes.fr/halshs-01003914
Contributor : Gilles Stoltz <>
Submitted on : Tuesday, June 10, 2014 - 9:00:48 PM
Last modification on : Friday, November 6, 2020 - 3:10:07 PM
Long-term archiving on: : Wednesday, September 10, 2014 - 12:25:51 PM

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ExchangeRates--HAL-June-10.pdf
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  • HAL Id : halshs-01003914, version 1

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Christophe Amat, Tomasz Michalski, Gilles Stoltz. Forecasting exchange rates better than the random walk thanks to machine learning techniques. 2014. ⟨halshs-01003914v1⟩

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