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Path Dependent Coordination of Expectations in Asset Pricing Experiments: a Behavioral Explanation

Author

Listed:
  • Agliari, A.

    (Catholic University, Piacenza)

  • Hommes, C.H.

    (University of Amsterdam)

  • Pecora, N.

    (Catholic University, Piacenza)

Abstract

In the learning-to-forecast laboratory experiments in Hommes et al. (2005), three different types of aggregate asset price behavior have been observed: monotonic convergence to the stable fundamental steady state, dampened price oscillations and permanent price oscillations. We present a simple behavioral 2-type heuristics switching model explaining individual as well as aggregate behavior in the experiment. Based on relative performance, agents switch between a simple trend-following and an anchor and adjustment heuristic that differ in how much weight is given to the long run average price level. The nonlinear switching model exhibits path dependence through co-existence of a locally stable fundamental steady state and a stable (quasi-)periodic orbits. Depending on initial states, agents coordinate individual expectations either on a stable fundamental steady state path or on almost self-fulfilling persistent price fluctuations around the fundamental steady state.

Suggested Citation

  • Agliari, A. & Hommes, C.H. & Pecora, N., 2015. "Path Dependent Coordination of Expectations in Asset Pricing Experiments: a Behavioral Explanation," CeNDEF Working Papers 15-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  • Handle: RePEc:ams:ndfwpp:15-05
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    3. Boone, Brecht & Quaghebeur, Ewoud, 2018. "Beyond rational expectations: The effects of heuristic switching in an Overlapping Generations model," Journal of Economic Behavior & Organization, Elsevier, vol. 155(C), pages 349-364.
    4. Schmitt, Noemi & Tuinstra, Jan & Westerhoff, Frank, 2017. "Side effects of nonlinear profit taxes in an evolutionary market entry model: Abrupt changes, coexisting attractors and hysteresis problems," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 15-38.
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    7. He, Xue-Zhong & Li, Kai & Wang, Chuncheng, 2016. "Volatility clustering: A nonlinear theoretical approach," Journal of Economic Behavior & Organization, Elsevier, vol. 130(C), pages 274-297.
    8. Xu, Hai-Chuan & Zhang, Wei & Xiong, Xiong & Wang, Xue & Zhou, Wei-Xing, 2021. "The double-edged role of social learning: Flash crash and lower total volatility," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 405-420.
    9. Sarah Mignot & Fabio Tramontana & Frank Westerhoff, 2021. "Speculative asset price dynamics and wealth taxes," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 641-667, December.
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    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium

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