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Prospect Theory in the Heterogeneous Agent Model

Author

Listed:
  • Jan Polach

    (London School of Economics and Political Science, Houghton Street, London WC2A 2AE, United Kingdom)

  • Jiri Kukacka

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic
    Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Pod Vodarenskou Vezi 4, 182 00, Prague, Czech Republic)

Abstract

Using the Heterogeneous Agent Model framework, we incorporate an extension based on Prospect Theory into a popular agent-based asset pricing model. The extension covers the phenomenon of loss aversion manifested in risk aversion and asymmetric treatment of gains and losses. Using Monte Carlo methods, we investigate behavior and statistical properties of the extended model and assess its relevance with respect to financial data and stylized facts. We show that the Prospect Theory extension keeps the essential underlying mechanics of the model intact, however, that it changes the model dynamics considerably. Stability of the model increases but the occurrence of the fundamental strategy is more extreme. Moreover, the extension shifts the model closer to the behavior of real-world stock markets.

Suggested Citation

  • Jan Polach & Jiri Kukacka, 2016. "Prospect Theory in the Heterogeneous Agent Model," Working Papers IES 2016/14, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2016.
    • Handle: RePEc:fau:wpaper:wp2016_14
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    Cited by:

    1. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    2. Kukacka, Jiri & Kristoufek, Ladislav, 2021. "Does parameterization affect the complexity of agent-based models?," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 324-356.
    3. Cafferata, Alessia & Tramontana, Fabio, 2022. "Disposition Effect and its outcome on endogenous price fluctuations," MPRA Paper 113904, University Library of Munich, Germany.
    4. Kukacka, Jiri & Kristoufek, Ladislav, 2020. "Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    5. Brianzoni, Serena & Campisi, Giovanni, 2020. "Dynamical analysis of a financial market with fundamentalists, chartists, and imitators," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).

    More about this item

    Keywords

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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