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Exchange rate predictability and dynamic Bayesian learning

Author

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  • Joscha Beckmann
  • Gary Koop
  • Dimitris Korobilis
  • Rainer Alexander Schüssler

Abstract

We consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross‐section.

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  • Joscha Beckmann & Gary Koop & Dimitris Korobilis & Rainer Alexander Schüssler, 2020. "Exchange rate predictability and dynamic Bayesian learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 410-421, June.
  • Handle: RePEc:wly:japmet:v:35:y:2020:i:4:p:410-421
    DOI: 10.1002/jae.2761
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    4. Svatopluk Kapounek & Zuzana Kučerová & Evžen Kočenda, 2022. "Selective Attention in Exchange Rate Forecasting," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 23(2), pages 210-229, May.
    5. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    6. Bak, Yuhyeon & Park, Cheolbeom, 2022. "Exchange rate predictability, risk premiums, and predictive system," Economic Modelling, Elsevier, vol. 116(C).
    7. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    8. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022. "Enhanced Bayesian Neural Networks for Macroeconomics and Finance," Papers 2211.04752, arXiv.org, revised Apr 2023.
    9. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    10. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    11. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
    12. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).
    13. Niklas Valentin Lehmann, 2023. "Forecasting skill of a crowd-prediction platform: A comparison of exchange rate forecasts," Papers 2312.09081, arXiv.org.
    14. Davidson, Sharada Nia, 2020. "Interdependence or contagion: A model switching approach with a focus on Latin America," Economic Modelling, Elsevier, vol. 85(C), pages 166-197.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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