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An Asset Pricing Model with Adaptive Heterogeneous Agents and Wealth Effects

  • Carl Chiarella
  • Xue-Zhong He
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 550)

Summary

The characterisation of agents' preferences by decreasing absolute risk aversion (DARA) and constant relative risk aversion (CRRA) are well documented in the literature and also supported in both empirical and experimental studies. This paper considers a financial market with heterogeneous agents having power utility functions, which are the only utility functions displaying both DARA and CRRA. By introducing a population weighted average wealth measure, we develop an adaptive model to characterise asset price dynamics as well as the evolution of population proportions and wealth dynamics. Some numerical simulations are included to illustrate the evolution of the wealth dynamics, market behaviour and market efficiency within the framework of heterogeneous agents.

Keywords

Asset Price Trading Strategy Risky Asset Asset Price Model Dividend Yield 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Anderson, S and de Palma A and Thisse J (1993) Discrete Choice Theory of Product Differentiation. MIT Press, Cambridge, MAGoogle Scholar
  2. 2.
    Brock W and Hommes C (1997) A rational route to randomness. Econometrica 65:1059–1095CrossRefGoogle Scholar
  3. 3.
    Brock, W and Hommes C (1998) Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22:1235–1274CrossRefGoogle Scholar
  4. 4.
    Bullard J and Duffy J (1999) Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs. Computational Economics 13:41–60CrossRefGoogle Scholar
  5. 5.
    Chiarella C (1992) The dynamics of speculative behaviour. Annals of Operations Research 37:101–123CrossRefGoogle Scholar
  6. 6.
    Chiarella C and He X (2001) Asset pricing and wealth dynamics under heterogeneous expectations. Quantitative Finance 1:509–526CrossRefGoogle Scholar
  7. 7.
    Chiarella C and He X (2002a) An adaptive model on asset pricing and wealth dynamics with heterogeneous trading strategies. School of Finance and Economics, University of Techonology Sydney. Working Paper No. 84.Google Scholar
  8. 8.
    Chiarella C and He X (2002b) Heterogeneous beliefs, risk and learning in a simple asset pricing model. Computational Economics 19:95–132CrossRefGoogle Scholar
  9. 9.
    Chiarella C and He X (2003a) Dynamics of beliefs and learning under al-processes — the heterogeneous case. Journal of Economic Dynamics and Control 27:503–531CrossRefGoogle Scholar
  10. 10.
    Chiarella C and He X (2003) Heterogeneous beliefs, risk and learning in a simple asset pricing model with a market maker. Macroeconomic Dynamics 7:503–536.Google Scholar
  11. 11.
    Day R and Huang W (1990) Bulls, bears and market sheep. Journal of Economic Behavior and Organization 14:299–329CrossRefGoogle Scholar
  12. 12.
    Farmer J (1999) Physicists attempt to scale the ivory towers of finance. Computing in Science and Engineering 1:26–39Google Scholar
  13. 13.
    Farmer J and Lo A (1999) Frontier of finance: Evolution and efficient markets. Proceedings of the National Academy of Sciences 96:9991–9992CrossRefGoogle Scholar
  14. 14.
    Franke R and Nesemann T (1999) Two destabilizing strategies may be jointly stabilizing. Journal of Economics 69:1–18CrossRefGoogle Scholar
  15. 15.
    Frankel F and Froot K (1987) Using survey data to test propositions regarding exchange rate expectations. American Economic Review 77:133–153Google Scholar
  16. 16.
    Gaunersdorfer A (2000) Endogenous fluctuations in a simple asset pricing model with heterogeneous agents. Journal of Economic Dynamics and Control 24:799–831CrossRefGoogle Scholar
  17. 17.
    Gaunersdorfer A and Hommes C (2000) A nonlinear structural model for volatility clustering. CeNDF, University of Amsterdam. Working Paper 00-02Google Scholar
  18. 18.
    Hommes C (2001) Financial markets as nonlinear adaptive evolutionary systems. Quantitative Finance 1:149–167CrossRefGoogle Scholar
  19. 19.
    LeBaron B (2000) Agent based computational finance: suggested readings and early research. Journal of Economic Dynamics and Control 24:679–702CrossRefGoogle Scholar
  20. 20.
    Levy M and Levy H (1996) The danger of assuming homogeneous expectations. Financial Analysts Journal 52(3):65–70CrossRefGoogle Scholar
  21. 21.
    Levy M and Levy H and Solomon S (1994) A microscopic model of the stock market. Economics Letters 45:103–111CrossRefGoogle Scholar
  22. 22.
    Levy M and Levy H and Solomon S (2000) Microscopic Simulation of Financial Markets—from investor behavior to market phenomena. Acadmic Press. SydneyGoogle Scholar
  23. 23.
    Lux T (1995) Herd behaviour, bubbles and crashes. Economic Journal 105:881–896CrossRefGoogle Scholar
  24. 24.
    Lux T (1997) Time variation of second moments from a noise trader/infection model. Journal of Economic Dynamics and Control 22:1–38CrossRefGoogle Scholar
  25. 25.
    Lux T (1998) The socioeconomic dynamics of speculative markets: Interacting agents, chaos, and the fat tails of return distributions. Journal of Economic Behavior and Organization 33:143–165CrossRefGoogle Scholar
  26. 26.
    Lux T and Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial markets. Nature 397(11):498–500CrossRefGoogle Scholar
  27. 27.
    Manski C and McFadden D (1981) Structural Analysis of Discrete Data with Econometric Applications. MIT PressGoogle Scholar
  28. 28.
    Zschischang E and Lux T (2001) Some new results on the Levy, Levy and Solomon microscopic stock market model. Physica A 291:563–573CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Carl Chiarella
    • 1
  • Xue-Zhong He
    • 1
  1. 1.University of TechnologySydneyAustralia

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