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Forecasting exchange rates using multivariate threshold models

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  • Huber Florian

    (Oesterreichische Nationalbank (OeNB), Otto-Wagner-Platz 3, 1090 Vienna, Austria)

Abstract

This paper investigates the ability of a broad range of non-linear time series models to forecast the EUR/USD exchange rate. Using a variant of the well-known Dornbusch (Dornbusch, R. 1976. “Expectations and Exchange Rate Dynamics.” Journal of Political Economy 84: 1161–1176.) model to guide the specific choice of covariates, we find improvements over the random walk for all time horizons considered. While the improvement in forecasting accuracy is rather muted at the critical 1-month ahead horizon, accuracy increases seem to be more pronounced for longer-term forecasts. In addition, we account for model and specification uncertainty by applying several combination rules. Along this dimension our results suggest that we can still improve upon the single best performing model by a large extent.

Suggested Citation

  • Huber Florian, 2016. "Forecasting exchange rates using multivariate threshold models," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 193-210, January.
    • Handle: RePEc:bpj:bejmac:v:16:y:2016:i:1:p:193-210:n:8
    DOI: 10.1515/bejm-2015-0032
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    References listed on IDEAS

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    Cited by:

    1. Huber, Florian, 2017. "Structural breaks in Taylor rule based exchange rate models — Evidence from threshold time varying parameter models," Economics Letters, Elsevier, vol. 150(C), pages 48-52.
    2. Huseyin Ince & Ali Fehim Cebeci & Salih Zeki Imamoglu, 2019. "An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 817-831, February.
    3. Niko Hauzenberger & Florian Huber, 2020. "Model instability in predictive exchange rate regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 168-186, March.
    4. Works, Richard Floyd, 2016. "Econometric modeling of exchange rate determinants by market classification: An empirical analysis of Japan and South Korea using the sticky-price monetary theory," MPRA Paper 76382, University Library of Munich, Germany.
    5. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    6. Tasadduq Imam, 2021. "Model selection for one‐day‐ahead AUD/USD, AUD/EUR forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1808-1824, April.
    7. Sercan Eraslan, 2019. "Asymmetric arbitrage trading on offshore and onshore renminbi markets," Empirical Economics, Springer, vol. 57(5), pages 1653-1675, November.
    8. Christina Christou & Rangan Gupta & Christis Hassapis & Tahir Suleman, 2018. "The role of economic uncertainty in forecasting exchange rate returns and realized volatility: Evidence from quantile predictive regressions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 705-719, November.
    9. Works, Richard & Haan, Perry, 2017. "An Empirical Study of Japanese and South Korean Exchange Rates Using the Sticky-Price Monetary Theory," MPRA Paper 77235, University Library of Munich, Germany.
    10. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    11. Martin Casta, 2022. "How Credit Improves the Exchange Rate Forecast," Working Papers 2022/7, Czech National Bank.
    12. Eraslan, Sercan, 2017. "Asymmetric arbitrage trading on offshore and onshore renminbi markets," Discussion Papers 13/2017, Deutsche Bundesbank.

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