Fundamentals and Exchange Rate Forecastability with Machine Learning Methods

Tomasz Michalski and Christophe Amat ()

No 1049, HEC Research Papers Series from HEC Paris

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 -- sequential adaptive ridge regression -- that prevent overfitting in-sample for better and more stable forecasting performance out-of-sample the authors 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.

Keywords: exchange rates; forecasting; machine learning; purchasing power parity; uncovered interest rate parity; monetary exchange rate models (search for similar items in EconPapers)
JEL-codes: C53 F31 F37 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2014-08-29
New Economics Papers: this item is included in nep-for and nep-mon
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1049

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