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On the Sources of Uncertainty in Exchange Rate Predictability

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  • Joseph P. Byrne
  • Dimitris Korobilis
  • Pinho J. Ribeiro

Abstract

We analyse the role of time-variation in coe¢ cients and other sources of un- certainty in exchange rate forecasting regressions. Our techniques incorporate the notion that the relevant set of predictors and their corresponding weights, change over time. We Önd that predictive models which allow for sudden, rather than smooth, changes in coe¢ cients signiÖcantly beat the random walk benchmark in out-of-sample forecasting exercise. Using an innovative variance decomposition scheme, we identify uncertainty in coe¢ cientsíestimation and uncertainty about the precise degree of coe¢ cientsívariability, as the main fac- tors hindering modelsíforecasting performance. The uncertainty regarding the choice of the predictor is small.

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  • Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2014. "On the Sources of Uncertainty in Exchange Rate Predictability," Working Papers 2014_16, Business School - Economics, University of Glasgow.
  • Handle: RePEc:gla:glaewp:2014_16
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    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.
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    13. Kharrat, Sabrine & Hammami, Yacine & Fatnassi, Ibrahim, 2020. "On the cross-sectional relation between exchange rates and future fundamentals," Economic Modelling, Elsevier, vol. 89(C), pages 484-501.
    14. Alisa Yusupova & Nicos G. Pavlidis & Efthymios G. Pavlidis, 2019. "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting," Papers 1912.04661, arXiv.org.
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    16. Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.
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    18. Ribeiro, Pinho J., 2017. "Selecting exchange rate fundamentals by bootstrap," International Journal of Forecasting, Elsevier, vol. 33(4), pages 894-914.
    19. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
    20. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
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    More about this item

    Keywords

    Instabilities; Exchange Rate Forecasting; Time-Varying Parameter Models; Bayesian Model Averaging; Forecast Combination; Financial Condi- tion Indexes; Bootstrap;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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