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Exchange Rate Fundamentals, Forecasting, And Speculation: Bayesian Models In Black Markets


  • Robert Gramacy
  • Samuel W. Malone
  • Enrique Ter Horst


SUMMARY Although speculative activity is central to black markets for currency, the out‐of‐sample performance of structural models in those settings is unknown. We substantially update the literature on empirical determinants of black market rates and evaluate the out‐of‐sample performance of linear models and non‐parametric Bayesian treed Gaussian process (BTGP) models against the random walk benchmark. Fundamentals‐based models outperform the benchmark in out‐of‐sample prediction accuracy and trading rule profitability measures given future values of fundamentals. In simulated real‐time trading exercises, however, the BTGP achieves superior realized profitability, accuracy and market timing, while linear models do no better than a random walk. Copyright © 2013 John Wiley & Sons, Ltd.

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  • Robert Gramacy & Samuel W. Malone & Enrique Ter Horst, 2014. "Exchange Rate Fundamentals, Forecasting, And Speculation: Bayesian Models In Black Markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 22-41, January.
  • Handle: RePEc:wly:japmet:v:29:y:2014:i:1:p:22-41

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    Cited by:

    1. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    2. Agust n S. B n trix & Hayley Pallan & Ugo Panizza, 2022. "The Elusive Link Between FDI and Economic Growth," Trinity Economics Papers tep0722, Trinity College Dublin, Department of Economics.
    3. William Easterly, 2019. "In Search of Reforms for Growth: New Stylized Facts on Policy and Growth Outcomes," NBER Working Papers 26318, National Bureau of Economic Research, Inc.
    4. Samuel W. Malone & Robert B. Gramacy & Enrique Ter Horst, 2016. "Timing Foreign Exchange Markets," Econometrics, MDPI, vol. 4(1), pages 1-23, March.

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