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Bounded Rationality and the Elasticity Puzzle: What Can We Learn from the Agent-Based Computational Consumption Capital Asset Pricing Model?

Author

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  • Ke-Hung Lai
  • Shu-Heng Chen
  • Ya-Chi Huang

Abstract

In this paper, an agent-based computational capital asset pricing model is applied to address an issue, known as the elasticity puzzle, originating from a famous reciprocal relation between the elasticity of intertemporal substitution (EIS) and the relative risk aversion (RRA) coefficient. By the reciprocal relation, the implied RRA coefficient can be unexpectedly, and possibly unacceptably, high when the estimated elasticity of EIS is so low and even closer to zero. Existing studies, be they theoretical or empirical, to deal with the elasticity puzzle are largely confined to the conventional framework built upon the devices of rational expectations and representative agents. A number of recent empirical studies, however, have documented heterogeneity in the elasticity. It is found that the intertemporal elasticity is different between the poor and the rich, and between stockholders and non-stockholders. Two questions immediately arise. The first one concerns the aggregation problem. If the intertemporal elasticity is heterogeneous among agents, then what is the relation between the aggregate elasticity and its individual counterparts. The second one is why the rich and the stockholders tend to have to high intertemporal elasticities, and their opposites tend to have low ones. Why is such a behavioral parameter so critical in determining the wealth share of individuals? Empirical studies also find that the Euler consumption equation applies well only to the stock market participants, and not to all individuals. It is certainly plausible that not all individuals can do optimization well. So, here comes the third question. Is it possible that some agents who happen to do optimization well and hence behave closer to what the Euler equation predicts eventually become wealthier, and for those who do not and hence fail the Euler equation eventually become poor? Do the rich really have higher intertemporal elasticities, or are they just smarter or with a better luck? Is that possible the observed heterogeneity in intertemporal ealsticity is just spurious? Using agent-based computational model, we study a consumption CAPM model composed of boundedly-rational heterogeneous agents. They are heterogeneous in their forecasts, saving and investment decisions, driven by an adaptive scheme, such as GAs. Simulating the model can generate a sequence of time-series observations of individuals' profiles, including beliefs, consumption, savings, and portfolios. Unlike most theoretical or empirical studies of the consumption CAPM model, the agent-based computational model do not assume an exogenously given stochastic process of returns and consumption. Instead, aggregate consumption, asset prices, and returns are also endogenously generated with agents under specified RRA and EIS. With this endogenously generated aggregate and individual data, we are better equipped to answer the three questions posed above in a fashion of survival dynamics

Suggested Citation

  • Ke-Hung Lai & Shu-Heng Chen & Ya-Chi Huang, 2005. "Bounded Rationality and the Elasticity Puzzle: What Can We Learn from the Agent-Based Computational Consumption Capital Asset Pricing Model?," Computing in Economics and Finance 2005 207, Society for Computational Economics.
  • Handle: RePEc:sce:scecf5:207
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    References listed on IDEAS

    as
    1. LeBaron, Blake, 2001. "Evolution And Time Horizons In An Agent-Based Stock Market," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 225-254, April.
    2. Motohiro Yogo, 2004. "Estimating the Elasticity of Intertemporal Substitution When Instruments Are Weak," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 797-810, August.
    3. Shu-Heng Chen & Chung-Chi Liao & Chi-Hsuan Yeh, 2000. "On The Emergent Properties Of Artificial Stock Markets: Some Initial Evidences," Computing in Economics and Finance 2000 328, Society for Computational Economics.
    4. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    5. Hansen, Lars Peter & Singleton, Kenneth J, 1983. "Stochastic Consumption, Risk Aversion, and the Temporal Behavior of Asset Returns," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 249-265, April.
    6. C. Chiarella & X-Z. He, 2001. "Asset price and wealth dynamics under heterogeneous expectations," Quantitative Finance, Taylor & Francis Journals, vol. 1(5), pages 509-526.
    7. Spear, Stephen E, 1989. "Learning Rational Expectations under Computability Constraints," Econometrica, Econometric Society, vol. 57(4), pages 889-910, July.
    8. Larry E. Jones & Rodolfo E. Manuelli & Ennio Stacchetti, 1999. "Technology (and Policy) Shocks in Models of Endogenous Growth," NBER Working Papers 7063, National Bureau of Economic Research, Inc.
    9. Weil, Philippe, 1989. "The equity premium puzzle and the risk-free rate puzzle," Journal of Monetary Economics, Elsevier, vol. 24(3), pages 401-421, November.
    10. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," Review of Economic Studies, Oxford University Press, vol. 25(2), pages 65-86.
    11. Chiarella, Carl & He, Xue-Zhong, 2002. "Heterogeneous Beliefs, Risk and Learning in a Simple Asset Pricing Model," Computational Economics, Springer;Society for Computational Economics, vol. 19(1), pages 95-132, February.
    12. Chiarella, Carl & He, Xue-Zhong, 2003. "Dynamics of beliefs and learning under aL-processes -- the heterogeneous case," Journal of Economic Dynamics and Control, Elsevier, vol. 27(3), pages 503-531, January.
    13. Hall, Robert E, 1988. "Intertemporal Substitution in Consumption," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 339-357, April.
    14. Smith, David C., 1999. "Finite sample properties of tests of the Epstein-Zin asset pricing model," Journal of Econometrics, Elsevier, vol. 93(1), pages 113-148, November.
    15. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 41-60, February.
    16. King, Robert G & Rebelo, Sergio, 1990. "Public Policy and Economic Growth: Developing Neoclassical Implications," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 126-150, October.
    17. Chiarella, Carl & He, Xue-Zhong, 2003. "Heterogeneous Beliefs, Risk, And Learning In A Simple Asset-Pricing Model With A Market Maker," Macroeconomic Dynamics, Cambridge University Press, vol. 7(4), pages 503-536, September.
    18. Alvaro Sandroni, 2000. "Do Markets Favor Agents Able to Make Accurate Predicitions?," Econometrica, Econometric Society, vol. 68(6), pages 1303-1342, November.
    19. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    20. Tesfatsion, Leigh, 2001. "Introduction to the special issue on agent-based computational economics," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 281-293, March.
    21. Chen, Shu-Heng & Huang, Ya-Chi, 2008. "Risk preference, forecasting accuracy and survival dynamics: Simulations based on a multi-asset agent-based artificial stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 702-717, September.
    22. Frank Westerhoff, 2003. "Heterogeneous traders and the Tobin tax," Journal of Evolutionary Economics, Springer, vol. 13(1), pages 53-70, February.
    23. Breeden, Douglas T., 1979. "An intertemporal asset pricing model with stochastic consumption and investment opportunities," Journal of Financial Economics, Elsevier, vol. 7(3), pages 265-296, September.
    24. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    25. Epstein, Larry G & Zin, Stanley E, 1991. "Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: An Empirical Analysis," Journal of Political Economy, University of Chicago Press, vol. 99(2), pages 263-286, April.
    26. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    27. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    28. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, vol. 58(1), pages 9-40, October.
    29. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
    30. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
    31. Michael Maschek & Jasmina Arifovic, 2003. "Expectations and Currency Crisis - An experimental approach," Computing in Economics and Finance 2003 245, Society for Computational Economics.
    32. Patterson, Kerry D & Pesaran, Bahram, 1992. "The Intertemporal Elasticity of Substitution in Consumption in the United States and the United Kingdom," The Review of Economics and Statistics, MIT Press, vol. 74(4), pages 573-584, November.
    33. Annette Vissing-Jørgensen & Orazio P. Attanasio, 2003. "Stock-Market Participation, Intertemporal Substitution, and Risk-Aversion," American Economic Review, American Economic Association, vol. 93(2), pages 383-391, May.
    34. Neely, Christopher J & Roy, Amlan & Whiteman, Charles H, 2001. "Risk Aversion versus Intertemporal Substitution: A Case Study of Identification Failure in the Intertemporal Consumption Capital Asset Pricing Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 395-403, October.
    35. Hansen, Lars Peter & Singleton, Kenneth J, 1982. "Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 50(5), pages 1269-1286, September.
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    More about this item

    Keywords

    Bounded Rationality; Elasticity Puzzle; Risk Preference; Consumption Capital Asset Pricing Model; Agent-Based Computational Modeling; Genetic Algorithms;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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