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Fundamentals and exchange rate forecastability with machine learning methods

  • Christophe Amat

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - CNRS - GROUPE HEC)

  • Tomasz Michalski

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - CNRS - GROUPE HEC)

  • Gilles Stoltz

    ()

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - CNRS - GROUPE HEC)

Using methods from machine learning we show that fundamentals from simple exchange rate models (PPP, UIRP and monetary models) 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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Paper provided by HAL in its series Working Papers with number halshs-01003914.

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Date of creation: 07 Oct 2015
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Handle: RePEc:hal:wpaper:halshs-01003914
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