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Asset pricing under ambiguous information: an empirical game-theoretic analysis

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  • Ben-Alexander Cassell

    (University of Michigan)

  • Michael P. Wellman

    (University of Michigan)

Abstract

In a representative agent model, the behavior of a social system is described in terms of a single aggregate decision maker. Such models are popular in economic and finance research, largely due to their analytic tractability, but fail to account for real-world agent heterogeneity, and may ignore effects of market microstructure. Agent-based simulation models naturally incorporate agent heterogeneity, and can account for any particular market microstructure; however, such models have gained only limited acceptance by the mainstream economic research community, due to concerns over how much general insight can be gleaned from simulating a particular configuration of agent behaviors. We address such concerns by employing game-theoretic criteria in behavior selection. We present a case study investigating a recent model from the finance literature proposed by Epstein and Schneider (ES), and its ability to explain the classic equity premium puzzle in risky asset pricing. For all market configurations that we examined, ambiguity-averse pricing was played with little or no probability in equilibrium. Moreover, none of the market configurations exhibited significant equity premia. Both our use of strategic equilibrium as a market composition concept, and the actions of our simulated market microstructure contribute to removing any equity premium. These findings underscore the value of checking that results from abstract representative-agent models are supportable in a higher-fidelity model where heterogeneity and strategic interactions are taken into account.

Suggested Citation

  • Ben-Alexander Cassell & Michael P. Wellman, 2012. "Asset pricing under ambiguous information: an empirical game-theoretic analysis," Computational and Mathematical Organization Theory, Springer, vol. 18(4), pages 445-462, December.
    • Handle: RePEc:spr:comaot:v:18:y:2012:i:4:d:10.1007_s10588-012-9133-y
    DOI: 10.1007/s10588-012-9133-y
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    References listed on IDEAS

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    1. Marco Raberto & Andrea Teglio & Silvano Cincotti, 2008. "Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 147-162, September.
    2. Leombruni, Roberto & Richiardi, Matteo, 2005. "Why are economists sceptical about agent-based simulations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 103-109.
    3. Larry G. Epstein & Martin Schneider, 2008. "Ambiguity, Information Quality, and Asset Pricing," Journal of Finance, American Finance Association, vol. 63(1), pages 197-228, February.
    4. J. Bradford DeLong & Konstantin Magin, 2009. "The U.S. Equity Return Premium: Past, Present, and Future," Journal of Economic Perspectives, American Economic Association, vol. 23(1), pages 193-208, Winter.
    5. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    6. Friedman, Daniel, 1991. "Evolutionary Games in Economics," Econometrica, Econometric Society, vol. 59(3), pages 637-666, May.
    7. Yoram Halevy, 2007. "Ellsberg Revisited: An Experimental Study," Econometrica, Econometric Society, vol. 75(2), pages 503-536, March.
    8. S. S. Lavenberg & P. D. Welch, 1981. "A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations," Management Science, INFORMS, vol. 27(3), pages 322-335, March.
    9. Martin L. Weitzman, 2007. "Subjective Expectations and Asset-Return Puzzles," American Economic Review, American Economic Association, vol. 97(4), pages 1102-1130, September.
    10. Rothschild, Michael & Stiglitz, Joseph E., 1970. "Increasing risk: I. A definition," Journal of Economic Theory, Elsevier, vol. 2(3), pages 225-243, September.
    11. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
    12. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    13. Gilboa, Itzhak & Schmeidler, David, 1989. "Maxmin expected utility with non-unique prior," Journal of Mathematical Economics, Elsevier, vol. 18(2), pages 141-153, April.
    14. David A. Chapman & Valery Polkovnichenko, 2009. "First‐Order Risk Aversion, Heterogeneity, and Asset Market Outcomes," Journal of Finance, American Finance Association, vol. 64(4), pages 1863-1887, August.
    15. Mehra, Rajnish & Prescott, Edward C., 1985. "The equity premium: A puzzle," Journal of Monetary Economics, Elsevier, vol. 15(2), pages 145-161, March.
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    Cited by:

    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 94-121, March.
    2. Steve Phelps & Wing Lon Ng, 2014. "A Simulation Analysis Of Herding And Unifractal Scaling Behaviour," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(1), pages 39-58, January.

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