HEC Research Papers Series,
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

JEL-codes: C53; F31; F37

57 pages, August 29, 2014

Full text files

papers.cfm?abstract_id=2448655  

Download statistics

• HEC Paris
• Ordering Working Papers
• Home page for this series

Questions (including download problems) about the papers in this series should be directed to Antoine Haldemann ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:ebg:heccah:1049This page generated on 2024-09-13 22:19:53.