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Fundamentals and Exchange Rate Forecastability with Simple Machine Learning Methods

77 Pages Posted: 12 Jun 2014 Last revised: 21 Jul 2017

Christophe Amat

Ecole Polytechnique, Paris

Tomasz Kamil Michalski

HEC Paris - Economics & Decision Sciences

Gilles Stoltz

HEC Paris - Economics & Decision Sciences

Date Written: June 26, 2017

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.

Keywords: exchange rates, forecasting, machine learning, purchasing power parity, uncovered interest rate parity, monetary exchange rate models

JEL Classification: C53, F31, F37

Suggested Citation

Amat, Christophe and Michalski, Tomasz Kamil and Stoltz, Gilles, Fundamentals and Exchange Rate Forecastability with Simple Machine Learning Methods (June 26, 2017). HEC Paris Research Paper No. ECO/SCD-2014-1049. Available at SSRN: https://ssrn.com/abstract=2448655 or http://dx.doi.org/10.2139/ssrn.2448655

Christophe Amat

Ecole Polytechnique, Paris ( email )

1 rue Descartes
Paris, 75005
France

Tomasz K. Michalski (Contact Author)

HEC Paris - Economics & Decision Sciences ( email )

Paris
France

Gilles Stoltz

HEC Paris - Economics & Decision Sciences ( email )

Paris
France

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