Fundamentals and Exchange Rate Forecastability with Simple Machine Learning Methods

69 Pages Posted: 12 Jun 2014 Last revised: 29 May 2018

See all articles by Christophe Amat

Christophe Amat

Ecole Polytechnique, Paris

Tomasz Kamil Michalski

HEC Paris - Economics & Decision Sciences

Gilles Stoltz

HEC Paris - Economics & Decision Sciences

Date Written: May 24, 2018

Abstract

Using methods from machine learning we show that fundamentals from simple exchange rate models (PPP or UIRP) or Taylor-rule based models lead to improved exchange rate forecasts for major currencies over the floating period era 1973--2014 at a 1-month forecast horizon which beat the no-change forecast. Fundamentals thus contain useful information and exchange rates are forecastable even for short horizons. Such conclusions cannot be obtained when using rolling or recursive OLS regressions as used 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 K. and Stoltz, Gilles, Fundamentals and Exchange Rate Forecastability with Simple Machine Learning Methods (May 24, 2018). 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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