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An Asset Pricing Model with Adaptive Heterogeneous Agents and Wealth Effects

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Nonlinear Dynamics and Heterogeneous Interacting Agents

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((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.

Acknowledgments. An early version of this paper was presented at the International Workshop on Economic Dynamics at the Lorentz Center at the University of Leiden, The Netherlands, June, 2002 and the 8th-International Conference of the Society for Computational Economics, July 2002, Aix-en-Procence, France. We would like to thank all participants for stimulating discussion. Special thanks are due to Thomas Lux for detailed comments and suggestion. The authors are responsible for any remaining errors in this paper. We also acknowledge the helpful comments of anonymous referee.

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References

  1. Anderson, S and de Palma A and Thisse J (1993) Discrete Choice Theory of Product Differentiation. MIT Press, Cambridge, MA

    Google Scholar 

  2. Brock W and Hommes C (1997) A rational route to randomness. Econometrica 65:1059–1095

    Article  Google Scholar 

  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 22:1235–1274

    Article  Google Scholar 

  4. Bullard J and Duffy J (1999) Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs. Computational Economics 13:41–60

    Article  Google Scholar 

  5. Chiarella C (1992) The dynamics of speculative behaviour. Annals of Operations Research 37:101–123

    Article  Google Scholar 

  6. Chiarella C and He X (2001) Asset pricing and wealth dynamics under heterogeneous expectations. Quantitative Finance 1:509–526

    Article  Google Scholar 

  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. Chiarella C and He X (2002b) Heterogeneous beliefs, risk and learning in a simple asset pricing model. Computational Economics 19:95–132

    Article  Google Scholar 

  9. Chiarella C and He X (2003a) Dynamics of beliefs and learning under al-processes — the heterogeneous case. Journal of Economic Dynamics and Control 27:503–531

    Article  Google Scholar 

  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. Day R and Huang W (1990) Bulls, bears and market sheep. Journal of Economic Behavior and Organization 14:299–329

    Article  Google Scholar 

  12. Farmer J (1999) Physicists attempt to scale the ivory towers of finance. Computing in Science and Engineering 1:26–39

    Google Scholar 

  13. Farmer J and Lo A (1999) Frontier of finance: Evolution and efficient markets. Proceedings of the National Academy of Sciences 96:9991–9992

    Article  Google Scholar 

  14. Franke R and Nesemann T (1999) Two destabilizing strategies may be jointly stabilizing. Journal of Economics 69:1–18

    Article  Google Scholar 

  15. Frankel F and Froot K (1987) Using survey data to test propositions regarding exchange rate expectations. American Economic Review 77:133–153

    Google Scholar 

  16. Gaunersdorfer A (2000) Endogenous fluctuations in a simple asset pricing model with heterogeneous agents. Journal of Economic Dynamics and Control 24:799–831

    Article  Google Scholar 

  17. Gaunersdorfer A and Hommes C (2000) A nonlinear structural model for volatility clustering. CeNDF, University of Amsterdam. Working Paper 00-02

    Google Scholar 

  18. Hommes C (2001) Financial markets as nonlinear adaptive evolutionary systems. Quantitative Finance 1:149–167

    Article  Google Scholar 

  19. LeBaron B (2000) Agent based computational finance: suggested readings and early research. Journal of Economic Dynamics and Control 24:679–702

    Article  Google Scholar 

  20. Levy M and Levy H (1996) The danger of assuming homogeneous expectations. Financial Analysts Journal 52(3):65–70

    Article  Google Scholar 

  21. Levy M and Levy H and Solomon S (1994) A microscopic model of the stock market. Economics Letters 45:103–111

    Article  Google Scholar 

  22. Levy M and Levy H and Solomon S (2000) Microscopic Simulation of Financial Markets—from investor behavior to market phenomena. Acadmic Press. Sydney

    Google Scholar 

  23. Lux T (1995) Herd behaviour, bubbles and crashes. Economic Journal 105:881–896

    Article  Google Scholar 

  24. Lux T (1997) Time variation of second moments from a noise trader/infection model. Journal of Economic Dynamics and Control 22:1–38

    Article  Google Scholar 

  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 33:143–165

    Article  Google Scholar 

  26. Lux T and Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial markets. Nature 397(11):498–500

    Article  Google Scholar 

  27. Manski C and McFadden D (1981) Structural Analysis of Discrete Data with Econometric Applications. MIT Press

    Google Scholar 

  28. Zschischang E and Lux T (2001) Some new results on the Levy, Levy and Solomon microscopic stock market model. Physica A 291:563–573

    Article  Google Scholar 

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Chiarella, C., He, XZ. (2005). An Asset Pricing Model with Adaptive Heterogeneous Agents and Wealth Effects. In: Lux, T., Samanidou, E., Reitz, S. (eds) Nonlinear Dynamics and Heterogeneous Interacting Agents. Lecture Notes in Economics and Mathematical Systems, vol 550. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27296-8_18

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