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Markov switching and exchange rate predictability

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  • Nikolsko-Rzhevskyy, Alex
  • Prodan, Ruxandra

Abstract

We first show that the recent success of modern macroeconomic models in forecasting nominal exchange rates, evaluated using the Clark and West (2006) inference procedure, is partly due to the presence of the constant term (drift), in addition to the economic fundamentals. We then model the drift term using the two-state Markov switching stochastic segmented trend model and present evidence of both short-run (one month) and long-run (up to one year) predictability for monthly exchange rates over the post-Bretton Woods period. This is an important result, as the recent literature has typically failed to find evidence of consistent multi-horizon predictability. The model strongly outperforms the random walk for 9 out of 12 exchange rate series at short horizons; for 7 of the 12 exchange rates, we find evidence of a long-run predictability that declines as the forecast horizon increases. Our results remain robust to alternative test statistics and forecast windows.

Suggested Citation

  • Nikolsko-Rzhevskyy, Alex & Prodan, Ruxandra, 2012. "Markov switching and exchange rate predictability," International Journal of Forecasting, Elsevier, vol. 28(2), pages 353-365.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:353-365
    DOI: 10.1016/j.ijforecast.2011.04.007
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    9. Panagiotis Papaioannnou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Papers 1310.5306, arXiv.org.
    10. 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.
    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. Alexandros Pasiouras & Theodoros Daglis, 2020. "The Dollar Exchange Rates in the Covid-19 Era: Evidence from 5 Currencies," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 352-361.
    13. Slim Chaouachi & Zied Ftiti & Frederic Teulon, 2014. "Explaining the Tunisian Real Exchange: Long Memory versus Structural Breaks," Working Papers 2014-147, Department of Research, Ipag Business School.
    14. Saldaña-Zepeda, Dayna P. & Velasco-Cruz, Ciro & Torres-Preciado, Víctor H., 2020. "Mexican peso-USD exchange rate: A switching linear dynamical model application," International Economics, Elsevier, vol. 162(C), pages 80-91.
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    16. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020. "Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models," Papers 2011.03741, arXiv.org, revised Dec 2020.
    17. Harris, Richard D. F. & Nguyen, Linh H & Stoja, Evarist, 2015. "Extreme downside risk and financial crises," Bank of England working papers 547, Bank of England.
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    20. repec:ipg:wpaper:2014-390 is not listed on IDEAS
    21. Konstantinos N. Konstantakis & Ioannis G. Melissaropoulos & Theodoros Daglis & Panayotis G. Michaelides, 2023. "The euro to dollar exchange rate in the Covid‐19 era: Evidence from spectral causality and Markov‐switching estimation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2037-2055, April.
    22. Panagiotis Papaioannou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Netnomics, Springer, vol. 14(1), pages 47-68, November.

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