Fundamentals and exchange rate forecastability with machine learning methods

Abstract : Using methods from machine learning we show that fundamentals from simple exchange rate models (PPP or UIRP) consistently allow to improve exchange rate forecasts for major currencies over the floating period era 1973--2014 at a 1 month forecast and allow to beat the no-change forecast. ``Classic'' fundamentals hence contain useful information and exchange rates are forecastable even for short forecasting horizons. Such conclusions cannot be obtained when using rolling or recursive OLS regressions as in the literature. The methods we use -- sequential ridge regression and the exponentially weighted average strategy both with discount factors -- do not estimate an underlying model but combine the fundamentals to directly output forecasts.
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Pré-publication, Document de travail
2016
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https://halshs.archives-ouvertes.fr/halshs-01003914
Contributeur : Gilles Stoltz <>
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Dernière modification le : samedi 18 février 2017 - 01:20:20
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  • HAL Id : halshs-01003914, version 5

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Christophe Amat, Tomasz Michalski, Gilles Stoltz. Fundamentals and exchange rate forecastability with machine learning methods. 2016. <halshs-01003914v5>

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