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Financial Market Dynamics: Superdiffusive or not?

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  • Sandhya Devi

Abstract

The behavior of stock market returns over a period of 1-60 days has been investigated for S&P 500 and Nasdaq within the framework of nonextensive Tsallis statistics. Even for such long terms, the distributions of the returns are non-Gaussian. They have fat tails indicating that the stock returns do not follow a random walk model. In this work, a good fit to a Tsallis q-Gaussian distribution is obtained for the distributions of all the returns using the method of Maximum Likelihood Estimate. For all the regions of data considered, the values of the scaling parameter q, estimated from one day returns, lie in the range 1.4 to 1.65. The estimated inverse mean square deviations (beta) show a power law behavior in time with exponent values between -0.91 and -1.1 indicating normal to mildly subdiffusive behavior. Quite often, the dynamics of market return distributions is modelled by a Fokker-Plank (FP) equation either with a linear drift and a nonlinear diffusion term or with just a nonlinear diffusion term. Both of these cases support a q-Gaussian distribution as a solution. The distributions obtained from current estimated parameters are compared with the solutions of the FP equations. For negligible drift term, the inverse mean square deviations (betaFP) from the FP model follow a power law with exponent values between -1.25 and -1.48 indicating superdiffusion. When the drift term is non-negligible, the corresponding betaFP do not follow a power law and become stationary after certain characteristic times that depend on the values of the drift parameter and q. Neither of these behaviors is supported by the results of the empirical fit.

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  • Sandhya Devi, 2016. "Financial Market Dynamics: Superdiffusive or not?," Papers 1608.07752, arXiv.org, revised Sep 2017.
    • Handle: RePEc:arx:papers:1608.07752
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    1. Cortines, A.A.G. & Riera, R., 2007. "Non-extensive behavior of a stock market index at microscopic time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 181-192.
    2. D. Sornette, 2003. "Critical Market Crashes," Papers cond-mat/0301543, arXiv.org.
    3. Constantino Tsallis & Celia Anteneodo & Lisa Borland & Roberto Osorio, 2003. "Nonextensive statistical mechanics and economics," Papers cond-mat/0301307, arXiv.org.
    4. Lisa Borland, 2002. "A theory of non-Gaussian option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 2(6), pages 415-431.
    5. V. Schwämmle & E. M.F. Curado & F. D. Nobre, 2009. "Dynamics of normal and anomalous diffusion in nonlinear Fokker-Planck equations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 70(1), pages 107-116, July.
    6. Pavlos, G.P. & Karakatsanis, L.P. & Xenakis, M.N. & Sarafopoulos, D. & Pavlos, E.G., 2012. "Tsallis statistics and magnetospheric self-organization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3069-3080.
    7. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    8. Tsallis, Constantino & Anteneodo, Celia & Borland, Lisa & Osorio, Roberto, 2003. "Nonextensive statistical mechanics and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 89-100.
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    Cited by:

    1. Devi, Sandhya, 2018. "Financial Portfolios based on Tsallis Relative Entropy as the Risk Measure," MPRA Paper 91614, University Library of Munich, Germany.
    2. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2018. "Nonextensive triplets in cryptocurrency exchanges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1069-1074.
    3. Sandhya Devi, 2019. "Financial Portfolios based on Tsallis Relative Entropy as the Risk Measure," Papers 1901.04945, arXiv.org, revised Mar 2019.
    4. Devi, Sandhya, 2021. "Asymmetric Tsallis distributions for modeling financial market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    5. Sandhya Devi, 2021. "Asymmetric Tsallis distributions for modelling financial market dynamics," Papers 2102.04532, arXiv.org.

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