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Forecasting exchange rates out of sample: random walk vs Markov switching regimes

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  • Dimitris Kirikos

Abstract

A random walk is compared with a Markov switching regimes process in forecasting exchange rates out of sample, using quarterly data on three currencies relative to the US dollar over the period 1973:3-1997:3. The results show that the relative performance of the models varies with the length of the post-sample period suggesting that the availability of more past information may be useful in forecasting future exchange rates.

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  • Dimitris Kirikos, 2000. "Forecasting exchange rates out of sample: random walk vs Markov switching regimes," Applied Economics Letters, Taylor & Francis Journals, vol. 7(2), pages 133-136.
    • Handle: RePEc:taf:apeclt:v:7:y:2000:i:2:p:133-136
    DOI: 10.1080/135048500351979
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    References listed on IDEAS

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    1. Woo, Wing T., 1985. "The monetary approach to exchange rate determination under rational expectations: The dollar-deutschmark rate," Journal of International Economics, Elsevier, vol. 18(1-2), pages 1-16, February.
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    7. Dimitris Kirikos, 1996. "The role of the forecast-generating process in assessing asset market models of the exchange rate: a non-linear case," The European Journal of Finance, Taylor & Francis Journals, vol. 2(2), pages 125-144.
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    Cited by:

    1. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
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    3. Chien-Hsiu Lin & Shih-Kuei Lin & An-Chi Wu, 2015. "Foreign exchange option pricing in the currency cycle with jump risks," Review of Quantitative Finance and Accounting, Springer, vol. 44(4), pages 755-789, May.
    4. Imad Moosa & Kelly Burns, 2016. "The random walk as a forecasting benchmark: drift or no drift?," Applied Economics, Taylor & Francis Journals, vol. 48(43), pages 4131-4142, September.
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    6. Michał Rubaszek & Paweł Skrzypczyński & Grzegorz Koloch, 2010. "Forecasting the Polish Zloty with Non-Linear Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(2), pages 151-167, March.
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    8. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    9. Lee, Hsiu-Yun & Chen, Show-Lin, 2006. "Why use Markov-switching models in exchange rate prediction?," Economic Modelling, Elsevier, vol. 23(4), pages 662-668, July.
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    11. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. "“An application of deep learning for exchange rate forecasting”," AQR Working Papers 202201, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2022.
    12. Stéphane Goutte & Raphaël Homayoun & Thomas Porcher, 2014. "A regime switching model to evaluate bonds in a quadratic term structure of interest rates," Working Papers hal-01090846, HAL.
    13. T. G. Saji, 2019. "Can BRICS Form a Currency Union? An Analysis under Markov Regime-Switching Framework," Global Business Review, International Management Institute, vol. 20(1), pages 151-165, February.
    14. Chien-Chung Nieh & Jeng-Bau Lin & Yu-Shan Wang, 2008. "Regime-switching analysis for the impacts of exchange rate volatility on corporate values: a Taiwanese case," Applied Economics, Taylor & Francis Journals, vol. 40(4), pages 491-504.
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    16. A. C. -L. Chian & E. L. Rempel & C. Rogers, 2007. "Crisis-induced intermittency in non-linear economic cycles," Applied Economics Letters, Taylor & Francis Journals, vol. 14(3), pages 211-218.

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