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The Qualitative Expectations Hypothesis: Model Ambiguity, Consistent Representations Of Market Forecasts, And Sentiment

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  • Roman Frydman

    (Department of Economics, New York University.)

  • Søren Johansen

    (Department of Economics, University of Copenhagen)

  • Anders Rahbek

    (Department of Economics, University of Copenhagen)

  • Morten Nyboe

    (Department of Economics, University of Copenhagen)

Abstract

We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and ?nancial outcomes. Building on John Muth?s seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market?s forecasts to be consistent with the predictions of an economist?s model. However, by assuming that outcomes lie within stochastic intervals, QEH, unlike REH, recognizes the ambiguity faced by an economist and market participants alike. Moreover, QEH leaves the model open to ambiguity by not specifying a mechanism determining speci?c values that outcomes take within these intervals. In order to examine a QEH model?s empirical relevance, we formulate and estimate its statistical analog based on simulated data. We show that the proposed statistical model adequately represents an illustrative sample from the QEH model. We also illustrate how estimates of the statistical model?s parameters can be used to assess the QEH model?s qualitative implications.

Suggested Citation

  • Roman Frydman & Søren Johansen & Anders Rahbek & Morten Nyboe, 2017. "The Qualitative Expectations Hypothesis: Model Ambiguity, Consistent Representations Of Market Forecasts, And Sentiment," Discussion Papers 17-10, University of Copenhagen. Department of Economics.
  • Handle: RePEc:kud:kuiedp:1710
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    References listed on IDEAS

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    1. F. Blasques & S. J. Koopman & A. Lucas, 2015. "Information-theoretic optimality of observation-driven time series models for continuous responses," Biometrika, Biometrika Trust, vol. 102(2), pages 325-343.
    2. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross-Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
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    4. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
    5. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
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    8. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
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    More about this item

    Keywords

    Asset-Price Movements; Model Ambiguity; Models with Time-Varying Parameters; REH; Behavioral Finance; GAS Models;

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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