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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

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 367.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:367

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Keywords: Exchange Rate Economics; Adaptive Learning; Behavioral Finance;

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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. Vivien Lewis & Agnieszka Markiewicz, 2009. "Model misspecification, learning and the exchange rate disconnect puzzle," Working Paper Research 168, National Bank of Belgium.
  5. 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.
  6. 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.
  7. Carlos Eduardo Iwai Drumond & Gilberto Tadeu Lim, 2014. "Exchange Rate Dynamics With Heterogeneous Expectations," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41th Brazilian Economics Meeting] 108, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].
  8. Mikael Bask, 2009. "Announcement effects on exchange rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 14(1), pages 64-84.
  9. Hommes, C.H., 2007. "Bounded Rationality and Learning in Complex Markets," CeNDEF Working Papers 07-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.

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