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Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?

Listed author(s):
  • Jiahan Li

    ()

    (University of Notre Dame, USA)

  • Ilias Tsiakas

    ()

    (University of Guelph, Canada)

  • Wei Wang

    ()

    (Fifth Third Bank, USA)

This paper shows that economic fundamentals can generate reliable out-of-sample forecasts for exchange rates when prediction is based on a "kitchen-sink" regression that incorporates multiple predictors. The key to establishing predictability is estimating the kitchen-sink regression with the elastic-net shrinkage method, which improves performance by reducing the effect of less informative predictors in out-of-sample forecasting. Using statistical and economic measures of predictability, we show that our approach outperforms alternative models, including the random walk, individual exchange rate models, a kitchen-sink regression estimated with ordinary least squares, standard forecast combinations and popular ad-hoc strategies such as momentum and the 1/N strategy.

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File URL: http://www.rcfea.org/RePEc/pdf/wp05_14.pdf
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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 05_14.

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Date of creation: Feb 2014
Publication status: Published in Journal of Financial Econometrics, 13(2):293-341, 2015
Handle: RePEc:rim:rimwps:05_14
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