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Information Dynamics In Financial Markets

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  • de Fontnouvelle, Patrick

Abstract

A noisy rational expectations model of asset trading is extended to incorporate costs of information acquisition and expectation formation. Because of the information costs, how much information to acquire becomes an important decision. Agents make this decision by choosing an expectations strategy about the future value of information. Because expectation formation is costly, agents often choose strategies that are simpler (and thus cheaper) than rational expectations. The model's dynamics can be expressed in terms of the market precision, which represents the amount of information acquired by the average agent. Under certain conditions, market precision follows an unstable and highly irregular time path. This irregularity directly affects observable market quantities. In particular, simulated time series for return volatility and trading volume display a copersistence similar to that found in actual financial data.

Suggested Citation

  • de Fontnouvelle, Patrick, 2000. "Information Dynamics In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(2), pages 139-169, June.
    • Handle: RePEc:cup:macdyn:v:4:y:2000:i:02:p:139-169_01
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    Cited by:

    1. Goldbaum, David & Mizrach, Bruce, 2008. "Estimating the intensity of choice in a dynamic mutual fund allocation decision," Journal of Economic Dynamics and Control, Elsevier, vol. 32(12), pages 3866-3876, December.
    2. Granger, Clive W.J. & Machina, Mark J., 2006. "Structural attribution of observed volatility clustering," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 15-29.
    3. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    4. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2007. "Agent-based Models of Financial Markets," Papers physics/0701140, arXiv.org.
    5. Brock, W.A. & Dindo, P.D.E. & Hommes, C.H., 2005. "Adaptive Rational Equilibrium with Forward Looking Agents, fortcoming in International Journal of Economic Theory (IJET) 2006, special issue in honor of Jean-Michel Grandmont," CeNDEF Working Papers 05-15, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    6. Hommes, C.H. & Wagener, F.O.O., 2008. "Complex evolutionary systems in behavioral finance," CeNDEF Working Papers 08-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    7. Hommes, C.H., 2005. "Heterogeneous Agents Models: two simple examples, forthcoming In: Lines, M. (ed.) Nonlinear Dynamical Systems in Economics, CISM Courses and Lectures, Springer, 2005, pp.131-164," CeNDEF Working Papers 05-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    8. Brock,W.A. & Hommes,C.H., 2001. "Evolutionary dynamics in financial markets with many trader types," Working papers 7, Wisconsin Madison - Social Systems.
    9. Brock, William A. & Hommes, Cars H. & Wagener, Florian O. O., 2005. "Evolutionary dynamics in markets with many trader types," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 7-42, February.
    10. Goldbaum, David, 2005. "Market efficiency and learning in an endogenously unstable environment," Journal of Economic Dynamics and Control, Elsevier, vol. 29(5), pages 953-978, May.
    11. Gerasymchuk, S. & Pavlov, O.V., 2010. "Asset Price Dynamics with Local Interactions under Heterogeneous Beliefs," CeNDEF Working Papers 10-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    12. Peter Christoffersen & Francis X. Diebold, 2002. "Financial Asset Returns, Market Timing, and Volatility Dynamics," CIRANO Working Papers 2002s-02, CIRANO.
    13. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.
    14. Andrea Gaunersdorfer & Cars Hommes, 2007. "A Nonlinear Structural Model for Volatility Clustering," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 265-288, Springer.
    15. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    16. Xue, Yi & Gençay, Ramazan, 2012. "Trading frequency and volatility clustering," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 760-773.
    17. Wang, Jianxin, 2022. "Market distraction and near-zero daily volatility persistence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    18. Brock,W.A. & Hommes,C.H., 2002. "Heterogeneous beliefs and routes to complex dynamics in asset pricing models with price contingent contracts," Working papers 3, Wisconsin Madison - Social Systems.
    19. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    20. Shimokawa, Tetsuya & Suzuki, Kyoko & Misawa, Tadanobu, 2007. "An agent-based approach to financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 207-225.
    21. Brock, W.A. & Hommes, C.H., 2001. "Heterogeneous beliefs and and routes to complez dynamics in asset pricing models with price contingent contracts," CeNDEF Working Papers 01-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    22. Friedman, Daniel & Abraham, Ralph, 2009. "Bubbles and crashes: Gradient dynamics in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 922-937, April.

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