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

Author

Listed:
  • Jiahan Li

    () (University of Notre Dame, USA)

  • Ilias Tsiakas

    () (University of Guelph, Canada)

  • Wei Wang

    () (Fifth Third Bank, USA)

Abstract

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.

Suggested Citation

  • Jiahan Li & Ilias Tsiakas & Wei Wang, 2014. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Working Paper series 05_14, Rimini Centre for Economic Analysis.
  • Handle: RePEc:rim:rimwps:05_14
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Li, Jiahan & Tsiakas, Ilias, 2017. "Equity premium prediction: The role of economic and statistical constraints," Journal of Financial Markets, Elsevier, vol. 36(C), pages 56-75.
    2. Buncic, Daniel & Piras, Gion Donat, 2016. "Heterogeneous agents, the financial crisis and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 313-359.
    3. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2014. "On the Sources of Uncertainty in Exchange Rate Predictability," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-24, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. repec:taf:wjabxx:v:18:y:2017:i:3:p:380-392 is not listed on IDEAS
    5. Bernard Njindan Iyke & Sin-Yu Ho, 2017. "The Real Exchange Rate, the Ghanaian Trade Balance, and the J-curve," Journal of African Business, Taylor & Francis Journals, vol. 18(3), pages 380-392, July.
    6. repec:eee:intfor:v:33:y:2017:i:4:p:894-914 is not listed on IDEAS
    7. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers 3214, Center for Quantitative Economics (CQE), University of Muenster.
    8. Anatolyev, Stanislav & Gospodinov, Nikolay & Jamali, Ibrahim & Liu, Xiaochun, 2015. "Foreign exchange predictability during the financial crisis: implications for carry trade profitability," FRB Atlanta Working Paper 2015-6, Federal Reserve Bank of Atlanta.
    9. repec:bla:ausecp:v:55:y:2016:i:4:p:409-433 is not listed on IDEAS
    10. Beckmann, Joscha & Schüssler, Rainer, 2016. "Forecasting exchange rates under parameter and model uncertainty," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 267-288.
    11. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    12. Tomáš Bunčák, 2016. "Exchange Rates Forecasting: Can Jump Models Combined with Macroeconomic Fundamentals Help?," Prague Economic Papers, University of Economics, Prague, vol. 2016(5), pages 527-546.
    13. repec:eee:empfin:v:42:y:2017:i:c:p:199-211 is not listed on IDEAS
    14. Tomáš Bunčák, . "Exchange Rates Forecasting: Can Jump Models Combined with Macroeconomic Fundamentals Help?," Prague Economic Papers, University of Economics, Prague, vol. 0, pages 1-20.

    More about this item

    Keywords

    Exchange Rates; Out-of-Sample Forecasting; Elastic Net; Combined Forecasts;

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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