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A Note on Forecasting Emerging Market Exchange Rates: Evidence of Anti-herding

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  • Christian Pierdzioch
  • Jan-Christoph Rülke
  • Georg Stadtmann

Abstract

Using survey forecasts of a large number of Asian, European, and South American emerging market exchange rates, we studied empirically whether evidence of herding or antiherding behavior of exchange-rate forecasters can be detected in the cross-section of forecasts. Emerging market exchange-rate forecasts are consistent with herding (anti-herding) if forecasts are biased towards (away from) the consensus forecast. Our empirical findings provide strong evidence of anti-herding of emerging market exchange-rate forecasters.
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  • Christian Pierdzioch & Jan-Christoph Rülke & Georg Stadtmann, 2012. "A Note on Forecasting Emerging Market Exchange Rates: Evidence of Anti-herding," Review of International Economics, Wiley Blackwell, vol. 20(5), pages 974-984, November.
    • Handle: RePEc:bla:reviec:v:20:y:2012:i:5:p:974-984
    DOI: roie.12007
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    Cited by:

    1. Pierdzioch, Christian & Rülke, Jan-Christoph, 2015. "On the directional accuracy of forecasts of emerging market exchange rates," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 369-376.
    2. Christoph Buehren & Tim Meyer & Christian Pierdzioch, 2020. "Experimental Evidence on Forecaster (anti-) Herding in Sports Markets," MAGKS Papers on Economics 202038, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    3. Lukas Richau & Florian Follert & Monika Frenger & Eike Emrich, 2021. "The sky is the limit?! Evaluating the existence of a speculative bubble in European football," Journal of Business Economics, Springer, vol. 91(6), pages 765-796, August.
    4. Fritsche, Ulrich & Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Forecasting the Brazilian real and the Mexican peso: Asymmetric loss, forecast rationality, and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 130-139.
    5. Pierdzioch, Christian & Reid, Monique B. & Gupta, Rangan, 2016. "Inflation forecasts and forecaster herding: Evidence from South African survey data," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 62(C), pages 42-50.
    6. Xolani Sibande & Rangan Gupta & Riza Demirer & Elie Bouri, 2023. "Investor Sentiment and (Anti) Herding in the Currency Market: Evidence from Twitter Feed Data," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 56-72, January.
    7. Hamid Baghestani & Liliana Danila, 2014. "Interest Rate and Exchange Rate Forecasting in the Czech Republic: Do Analysts Know Better than a Random Walk?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(4), pages 282-295, September.
    8. Michael P. Clements, 2018. "Do Macroforecasters Herd?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 265-292, March.
    9. Frenkel, Michael & Mauch, Matthias & Rülke, Jan-Christoph, 2020. "Do forecasters of major exchange rates herd?," Economic Modelling, Elsevier, vol. 84(C), pages 214-221.
    10. Tsuchiya, Yoichi, 2015. "Herding behavior and loss functions of exchange rate forecasters over interventions and financial crises," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 266-276.
    11. Yoichi Tsuchiya, 2021. "Crises, market shocks, and herding behavior in stock price forecasts," Empirical Economics, Springer, vol. 61(2), pages 919-945, August.
    12. Rybacki Jakub, 2020. "Macroeconomic forecasting in Poland: The role of forecasting competitions," Central European Economic Journal, Sciendo, vol. 7(54), pages 1-11, January.

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    More about this item

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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