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Fundamentals and exchange rate forecastability with machine learning methods

Author

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  • Christophe Amat

    () (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Tomasz Michalski

    () (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Gilles Stoltz

    () (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

Abstract

Using methods from machine learning we show that fundamentals from simple exchange rate models (PPP or UIRP) consistently allow to improve exchange rate forecasts for major currencies over the floating period era 1973--2014 at a 1 month forecast and allow to beat the no-change forecast. ``Classic'' fundamentals hence contain useful information and exchange rates are forecastable even for short forecasting horizons. Such conclusions cannot be obtained when using rolling or recursive OLS regressions as in the literature. The methods we use -- sequential ridge regression and the exponentially weighted average strategy both with discount factors -- do not estimate an underlying model but combine the fundamentals to directly output forecasts.

Suggested Citation

  • Christophe Amat & Tomasz Michalski & Gilles Stoltz, 2016. "Fundamentals and exchange rate forecastability with machine learning methods," Working Papers halshs-01003914, HAL.
  • Handle: RePEc:hal:wpaper:halshs-01003914
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01003914v5
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    References listed on IDEAS

    as
    1. Rossi, Barbara, 2006. "Are Exchange Rates Really Random Walks? Some Evidence Robust To Parameter Instability," Macroeconomic Dynamics, Cambridge University Press, vol. 10(01), pages 20-38, February.
    2. Engel, Charles, 1994. "Can the Markov switching model forecast exchange rates?," Journal of International Economics, Elsevier, vol. 36(1-2), pages 151-165, February.
    3. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    4. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    5. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    6. Rapach, David E. & Wohar, Mark E., 2002. "Testing the monetary model of exchange rate determination: new evidence from a century of data," Journal of International Economics, Elsevier, vol. 58(2), pages 359-385, December.
    7. Pierre-Olivier Gourinchas & Hélène Rey, 2007. "International Financial Adjustment," Journal of Political Economy, University of Chicago Press, vol. 115(4), pages 665-703, August.
    8. Pasquale Della Corte & Lucio Sarno & Giulia Sestieri, 2012. "The Predictive Information Content of External Imbalances for Exchange Rate Returns: How Much Is It Worth?," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 100-115, February.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Philippe Bacchetta & Eric van Wincoop & Toni Beutler, 2010. "Can Parameter Instability Explain the Meese-Rogoff Puzzle?," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 6(1), pages 125-173.
    11. Cerra, Valerie & Saxena, Sweta Chaman, 2010. "The monetary model strikes back: Evidence from the world," Journal of International Economics, Elsevier, vol. 81(2), pages 184-196, July.
    12. Wright, Jonathan H., 2008. "Bayesian Model Averaging and exchange rate forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 329-341, October.
    13. Tanya Molodtsova & Alex Nikolsko‐Rzhevskyy & David H. Papell, 2011. "Taylor Rules and the Euro," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43, pages 535-552, March.
    14. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia, 2005. "Empirical exchange rate models of the nineties: Are any fit to survive?," Journal of International Money and Finance, Elsevier, vol. 24(7), pages 1150-1175, November.
    15. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    16. Mark, Nelson C. & Sul, Donggyu, 2001. "Nominal exchange rates and monetary fundamentals: Evidence from a small post-Bretton woods panel," Journal of International Economics, Elsevier, vol. 53(1), pages 29-52, February.
    17. Schinasi, Garry J. & Swamy, P. A. V. B., 1989. "The out-of-sample forecasting performance of exchange rate models when coefficients are allowed to change," Journal of International Money and Finance, Elsevier, vol. 8(3), pages 375-390, September.
    18. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.
    19. Engel, Charles & West, Kenneth D., 2006. "Taylor Rules and the Deutschmark: Dollar Real Exchange Rate," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1175-1194, August.
    20. Molodtsova, Tanya & Papell, David H., 2009. "Out-of-sample exchange rate predictability with Taylor rule fundamentals," Journal of International Economics, Elsevier, vol. 77(2), pages 167-180, April.
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    Keywords

    uncovered interest rate parity; monetary exchange rate models; purchasing power parity; machine learning; forecasting; exchange rates;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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