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Nonlinear expectations in speculative markets - Evidence from the ECB survey of professional forecasters

Author

Listed:
  • Reitz, Stefan

    (Institute for Quantitative Business and Economics Research)

  • Rülke, Jan-Christoph

    (Department of Economics)

  • Stadtmann, Georg

    (Department of Business and Economics)

Abstract
Chartist and fundamentalist models have proven to be capable of replicating stylized facts on speculative markets. In general, this is achieved by specifying nonlinear interactions of otherwise linear asset price expectations of the respective trader groups. This paper investigates whether or not regressive and extrapolative expectations themselves exhibit significant nonlinear dynamics. The empirical results are based on a new data set from the European Central Bank Survey of Professional Forecasters on oil price expectations. In particular, we find that forecasters form destabilizing expectations in the neighborhood of the fundamental value, whereas expectations tend to be stabilizing in the presence of substantial oil price misalignment.

Suggested Citation

  • Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets - Evidence from the ECB survey of professional forecasters," Discussion Papers on Economics 1/2012, University of Southern Denmark, Department of Economics.
    • Handle: RePEc:hhs:sdueko:2012_001
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Atalla, Tarek & Joutz, Fred & Pierru, Axel, 2016. "Does disagreement among oil price forecasters reflect volatility? Evidence from the ECB surveys," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1178-1192.
    2. Leppin, Julian Sebastian, 2014. "The relation between overreaction in forecasts and uncertainty: A nonlinear approachvon," HWWI Research Papers 158, Hamburg Institute of International Economics (HWWI).
    3. Czudaj, Robert L., 2019. "Crude oil futures trading and uncertainty," Energy Economics, Elsevier, vol. 80(C), pages 793-811.
    4. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
    5. Zhenxi Chen & Stefan Reitz, 2020. "Dynamics of the European sovereign bonds and the identification of crisis periods," Empirical Economics, Springer, vol. 58(6), pages 2761-2781, June.
    6. Czudaj, Robert L., 2022. "Heterogeneity of beliefs and information rigidity in the crude oil market: Evidence from survey data," European Economic Review, Elsevier, vol. 143(C).
    7. Pierdzioch, Christian & Reitz, Stefan & Ruelke, Jan-Christoph, 2014. "Heterogeneous forecasters and nonlinear expectation formation in the US stock market," Kiel Working Papers 1947, Kiel Institute for the World Economy (IfW Kiel).
    8. Goldbaum, David & Zwinkels, Remco C.J., 2014. "An empirical examination of heterogeneity and switching in foreign exchange markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 667-684.
    9. Dick, Christian D. & Menkhoff, Lukas, 2013. "Exchange rate expectations of chartists and fundamentalists," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages 1362-1383.
    10. Leppin, Julian Sebstian, 2014. "The Relation Between Overreaction in Forecasts and Uncertainty: A Nonlinear Approach," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100284, Verein für Socialpolitik / German Economic Association.
    11. Imane El Ouadghiri, 2015. "Heterogeneity in Macroeconomic News Expectations: A disaggregate level analysis," EconomiX Working Papers 2015-17, University of Paris Nanterre, EconomiX.
    12. Imane El Ouadghiri, 2015. "Heterogeneity in Macroeconomic News Expectations: A disaggregate level analysis," Working Papers hal-04141409, HAL.

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    More about this item

    Keywords

    Agent based models; nonlinear expectations; survey data;
    All these keywords.

    JEL classification:

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

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