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Learning to Forecast the Exchange Rate: Two Competing Approaches

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  • Paul De Grauwe

    (KULeuven)

  • Agnieszka Markiewicz

    (KULeuven)

Abstract

In this paper, we investigate the behavior of the exchange rate within the framework of an asset pricing model. We assume boundedly rational agents who use simple rules to forecast the future exchange rate. They test these rules continuously using two learning mechanisms. The first one, the fitness method, assumes that agents evaluate forecasts by computing their past profitability. In the second mechanism, agents learn to improve these rules using statistical methods. First, we find that both learning mechanisms reveal the fundamental value of the exchange rate in the steady state. Second, both mechanisms mimic regularities observed in the foreign exchange markets, namely exchange rate disconnect and excess volatility. Fitness learning rule generates the disconnection at different frequencies, while the statistical method has this ability only at the high frequencies. Statistical learning can produce excess volatility of magnitude closer to reality than fitness learning but can also lead to explosive solutions

Suggested Citation

  • Paul De Grauwe & Agnieszka Markiewicz, 2006. "Learning to Forecast the Exchange Rate: Two Competing Approaches," Computing in Economics and Finance 2006 367, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:367
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    Cited by:

    1. de Zwart, Gerben & Markwat, Thijs & Swinkels, Laurens & van Dijk, Dick, 2009. "The economic value of fundamental and technical information in emerging currency markets," Journal of International Money and Finance, Elsevier, vol. 28(4), pages 581-604, June.
    2. repec:eee:intfin:v:48:y:2017:i:c:p:82-98 is not listed on IDEAS
    3. Matteo G. Richiardi, 2015. "The future of agent-based modelling," LABORatorio R. Revelli Working Papers Series 141, LABORatorio R. Revelli, Centre for Employment Studies.
    4. de Truchis, Gilles & Dell’Eva, Cyril & Keddad, Benjamin, 2017. "On exchange rate comovements: New evidence from a Taylor rule fundamentals model with adaptive learning," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 48(C), pages 82-98.
    5. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    6. Cars H. Hommes, 2009. "Bounded Rationality and Learning in Complex Markets," Chapters,in: Handbook of Research on Complexity, chapter 5 Edward Elgar Publishing.
    7. Teneng, Dean, 2013. "Outperforming the naïve Random Walk forecast of foreign exchange daily closing prices using Variance Gamma and normal inverse Gaussian Levy processes," MPRA Paper 47851, University Library of Munich, Germany.
    8. Goldbaum, David & Zwinkels, Remco C.J., 2014. "An empirical examination of heterogeneity and switching in foreign exchange markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 667-684.
    9. Matteo G. Richiardi, 2017. "The Future of Agent-Based Modeling," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 271-287, March.
    10. Lewis Vivien & Markiewicz Agnieszka, 2009. "Model Misspecification, Learning and the Exchange Rate Disconnect Puzzle," The B.E. Journal of Macroeconomics, De Gruyter, vol. 9(1), pages 1-24, April.
    11. Carlos Eduardo Iwai Drumond & Gilberto Tadeu Lim, 2014. "Exchange Rate Dynamics With Heterogeneous Expectations," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 108, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    12. Kim, Young Se, 2009. "Exchange rates and fundamentals under adaptive learning," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 843-863, April.
    13. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    14. ter Ellen, Saskia & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2013. "Dynamic expectation formation in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 75-97.
    15. Mikael Bask, 2009. "Announcement effects on exchange rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 14(1), pages 64-84.
    16. Andrea Giusto, 2015. "Learning to Agree: A New Perspective on Price Drift," Economics Bulletin, AccessEcon, vol. 35(1), pages 276-282.
    17. Kunze, Frederik, 2017. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," Center for European, Governance and Economic Development Research Discussion Papers 326, University of Goettingen, Department of Economics.
    18. Baur, Dirk G. & Glover, Kristoffer J., 2014. "Heterogeneous expectations in the gold market: Specification and estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 116-133.

    More about this item

    Keywords

    Exchange Rate Economics; Adaptive Learning; Behavioral Finance;

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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