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Forecasting disconnected exchange rates

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  • Travis J. Berge

Abstract

Catalyzed by the work of Meese and Rogoff (1983), a large literature has documented the inability of empirical models to accurately forecast exchange rates out-of-sample. This paper extends the literature by introducing an empirical strategy that endogenously builds forecast models from a broad set of conventional exchange rate signals. The method is extremely flexible, allowing for potentially nonlinear models for each currency and forecast horizon that evolve over time. Analysis of the models selected by the procedure sheds light on the erratic behavior of exchange rates and their apparent disconnect from macroeconomic fundamentals. In terms of forecast ability, the Meese-Rogoff result remains intact. At short horizons, the method cannot outperform a random walk, although at longer horizons the method does outperform the random walk null. These findings are found consistently across currencies and forecast evaluation methods.

Suggested Citation

  • Travis J. Berge, 2011. "Forecasting disconnected exchange rates," Research Working Paper RWP 11-12, Federal Reserve Bank of Kansas City, revised 2011.
  • Handle: RePEc:fip:fedkrw:rwp11-12
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    Cited by:

    1. Ribeiro, Pinho J., 2017. "Selecting exchange rate fundamentals by bootstrap," International Journal of Forecasting, Elsevier, vol. 33(4), pages 894-914.
    2. 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.
    3. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    4. 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.
    5. 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.
    6. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    7. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
    8. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.

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