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A behavioral asset pricing model with a time-varying second moment

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  • Chiarella, Carl
  • He, Xue-Zhong
  • Wang, Duo

Abstract

We develop a simple behavioral asset pricing model with fundamentalists and chartists in order to study price behavior in financial markets when chartists estimate both conditional mean and variance by using a weighted averaging process. Through a stability, bifurcation, and normal form analysis, the market impact of the weighting process and time-varying second moment are examined. It is found that the fundamental price becomes stable (unstable) when the activities from both types of traders are balanced (unbalanced). When the fundamental price becomes unstable, the weighting process leads to different price dynamics, depending on whether the chartists act as either trend followers or contrarians. It is also found that a time-varying second moment of the chartists does not change the stability of the fundamental price, but it does influence the stability of the bifurcations. The bifurcation becomes stable (unstable) when the chartists are more (less) concerned about the market risk characterized by the time-varying second moment. Different routes to complicated price dynamics are also observed. The analysis provides an analytical foundation for the statistical analysis of the corresponding stochastic version of this type of behavioral model.

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  • Chiarella, Carl & He, Xue-Zhong & Wang, Duo, 2006. "A behavioral asset pricing model with a time-varying second moment," Chaos, Solitons & Fractals, Elsevier, vol. 29(3), pages 535-555.
    • Handle: RePEc:eee:chsofr:v:29:y:2006:i:3:p:535-555
    DOI: 10.1016/j.chaos.2005.08.068
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    Cited by:

    1. Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yongjie Zhang & Wei Chen & Wei-Xing Zhou, 2021. "An empirical behavioral order-driven model with price limit rules," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    2. Naimzada, Ahmad K. & Ricchiuti, Giorgio, 2009. "Dynamic effects of increasing heterogeneity in financial markets," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1764-1772.
    3. Carl Chiarella & Roberto Dieci & Xue-Zhong He, 2008. "Heterogeneity, Market Mechanisms, and Asset Price Dynamics," Research Paper Series 231, Quantitative Finance Research Centre, University of Technology, Sydney.
    4. Melecký, Jan & Sergyeyev, Artur, 2008. "A simple finite-difference stock market model involving intrinsic value," Chaos, Solitons & Fractals, Elsevier, vol. 38(3), pages 769-777.
    5. Kousik Guhathakurtha, 2013. "Investigating The Nonlinear Dynamics Of Emerging And Developed Stock Markets," Working papers 142, Indian Institute of Management Kozhikode.
    6. Loretti I. Dobrescu & Mihaela Neamtu & Dumitru Opris, 2011. "A Discrete--Delay Dynamic Model for the Stock Market," Discussion Papers 2012-11, School of Economics, The University of New South Wales.
    7. Carl Chiarella & Xue-Zhong He & Duo Wang, 2004. "Statistical Properties of a Heterogeneous Asset Price Model with Time-Varying Second Moment," Research Paper Series 142, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    9. Charles S. Tapiero, 2015. "A financial CCAPM and economic inequalities," Quantitative Finance, Taylor & Francis Journals, vol. 15(3), pages 521-534, March.
    10. Ke, Xiaoling & Shi, Ke, 2009. "Stability and bifurcation in a simple heterogeneous asset pricing model," Economic Modelling, Elsevier, vol. 26(3), pages 680-688, May.
    11. Zhu, Mei & Wang, Duo & Guo, Maozheng, 2011. "Stochastic equilibria of an asset pricing model with heterogeneous beliefs and random dividends," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 131-147, January.
    12. Dieci, Roberto & Westerhoff, Frank, 2016. "Heterogeneous expectations, boom-bust housing cycles, and supply conditions: A nonlinear economic dynamics approach," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 21-44.
    13. Park, Beum-Jo, 2014. "Time-varying, heterogeneous risk aversion and dynamics of asset prices among boundedly rational agents," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 150-159.
    14. Tae-Seok Jang, 2015. "Identification of Social Interaction Effects in Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 207-238, February.
    15. Giovanni Campisi & Silvia Muzzioli, 2020. "Fundamentalists heterogeneity and the role of the sentiment indicator," Department of Economics 0167, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".

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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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