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Bifurcation Routes to Volatility Clustering

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  • Gaunersdorfer, A.

    () (University of Vienna)

  • Hommes, C.H.

    () (Universiteit van Amsterdam)

  • Wagener, F.O.O.

    () (Universiteit van Amsterdam)

Abstract

See also the publication 'Bifurcation Routes to Volatility Clustering under Evolutionary Learning' in the 'Journal of Economic Behavior & Organization'. Volume 67(1), pp. 27-47. A simple asset pricing model with two types of adaptively learning traders,fundamentalists and technical analysts, is studied. Fractions of these tradertypes, which are both boundedly rational, change over time according toevolutionary learning, with technical analysts conditioning their forecastingrule upon deviations from a benchmark fundamental. Volatility clustering arisesendogenously in this model. Two mechanisms are proposed as an explanation. Thefirst is coexistence of a stable steady state and a stable limit cycle, whicharise as a consequence of a so-called Chenciner bifurcation of the system. Thesecond is intermittency and associated bifurcation routes to strange attractors.Both phenomena are persistent and occur generically in nonlinearmulti-agent evolutionary systems.
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Suggested Citation

  • Gaunersdorfer, A. & Hommes, C.H. & Wagener, F.O.O., 2000. "Bifurcation Routes to Volatility Clustering," CeNDEF Working Papers 00-04, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  • Handle: RePEc:ams:ndfwpp:00-04
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    References listed on IDEAS

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    Cited by:

    1. Mikhail Anufriev & Cars Hommes, 2012. "Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments," American Economic Journal: Microeconomics, American Economic Association, vol. 4(4), pages 35-64, November.
    2. Hommes, C.H., 2001. "Modeling the stylized facts in finance through simple nonlinear adaptive systems," CeNDEF Working Papers 01-06, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    3. 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.
    4. Brock,W.A. & Hommes,C.H., 2001. "Evolutionary dynamics in financial markets with many trader types," Working papers 7, Wisconsin Madison - Social Systems.
    5. Gaunersdorfer, Andrea & Hommes, Cars H. & Wagener, Florian O.O., 2008. "Bifurcation routes to volatility clustering under evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 27-47, July.
    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. Roberto Dieci & Ilaria Foroni & Laura Gardini & Xue-Zhong He, 2005. "Market Mood, Adaptive Beliefs and Asset Price Dynamics," Research Paper Series 162, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. Michael Neugart & Jan Tuinstra, 2003. "Endogenous fluctuations in the demand for education," Journal of Evolutionary Economics, Springer, vol. 13(1), pages 29-51, February.
    9. Daniela Federici & Giancarlo Gandolfo, 2011. "The Euro/Dollar Exchange Rate: Chaotic or Non-Chaotic?," CESifo Working Paper Series 3420, CESifo Group Munich.
    10. Hammad A. Siddiqi, 2006. "Is it Social Influence on Beliefs Under Ambiguity? A Possible Explanation for Volatility Clustering," Microeconomics Working Papers 22279, East Asian Bureau of Economic Research.
    11. Alfarano, Simone & Lux, Thomas, 2003. "A minimal noise trader model with realistic time series properties," Economics Working Papers 2003-15, Christian-Albrechts-University of Kiel, Department of Economics.
    12. Carl Chiarella & Xue-Zhong He & Duo Wang, 2004. "A Behavioural Asset Pricing Model with a Time-Varying Second Moment," Research Paper Series 141, Quantitative Finance Research Centre, University of Technology, Sydney.
    13. 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.
    14. S. Borovkova & H. Dehling & J. Renkema & H. Tulleken, 2003. "A Potential-Field Approach to Financial Time Series Modelling," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 139-161, October.
    15. 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.
    16. Andrea Morone, 2008. "Financial markets in the laboratory: an experimental analysis of some stylized facts," Quantitative Finance, Taylor & Francis Journals, vol. 8(5), pages 513-532.
    17. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.
    18. Alfarano, Simone & Lux, Thomas, 2007. "A Noise Trader Model As A Generator Of Apparent Financial Power Laws And Long Memory," Macroeconomic Dynamics, Cambridge University Press, vol. 11(S1), pages 80-101, November.
    19. De Grauwe, Paul & Grimaldi, Marianna, 2006. "Exchange rate puzzles: A tale of switching attractors," European Economic Review, Elsevier, vol. 50(1), pages 1-33, January.
    20. Ilaria Foroni & Anna Agliari, 2008. "Complex Price Dynamics in a Financial Market with Imitation," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 21-36, September.
    21. Taisei Kaizoji, 2003. "Speculative bubbles and fat tail phenomena in a heterogeneous agent model," Papers nlin/0312040, arXiv.org.
    22. Verbic, Miroslav, 2006. "Memory and Asset Pricing Models with Heterogeneous Beliefs," MPRA Paper 1261, University Library of Munich, Germany.
    23. Siddiqi, Hammad, 2006. "Belief merging and revision under social influence: An explanation for the volatility clustering puzzle," MPRA Paper 657, University Library of Munich, Germany.

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