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Market characteristics and chaos dynamics in stock markets: an international comparison

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Mattarocci, Gianluca

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Abstract

The chaos theory assumes that the returns dynamics are not normally distributed and more complex approaches have to be used to study these time series. In fact, the Fractal Market Hypothesis assumes that the returns dynamics are not independent of the investors’ attitudes and represent the result of the interaction of traders who, frequently, adopt different investment styles. The studies proposed in literature try to identify the best approach to define the fractal dimension using, in particular, data of highly developed financial markets where a more complete set of information is available and the price determination mechanism is more efficient. A fault found with these approaches is that the results do not allow making out if there is a relationship between fractal dimension and market characteristics and, besides, it is hard to understand which aspects are more relevant in the definition of the fractal market dimension. In fact, previous studies analysed market liquidity for a limited number of countries and no other aspects related to market transactions have been considered. Using a large sample of world stock indexes, I try to identify the main market characteristics that influence returns dynamics. This study, carried out having recourse to the Rescaled Range Analysis (R/S) approach, shows that markets characteristic, like liquidity, type of admissible orders and so on, influence the R/S capability to study returns dynamics.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 4296.

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Date of creation: Jun 2006
Date of revision: Jun 2006
Handle: RePEc:pra:mprapa:4296

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Related research
Keywords: Chaos; fractal dimension; R/S analysis and market characteristics;

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Find related papers by JEL classification:
D49 - Microeconomics - - Market Structure and Pricing - - - Other
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation

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  1. Sadique, Shibley & Silvapulle, Param, 2001. "Long-Term Memory in Stock Market Returns: International Evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 59-67, January. [Downloadable!] (restricted)
  2. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July. [Downloadable!] (restricted)
  3. Fabrizio Lillo & J. Doyne Farmer, 2004. "The Long Memory of the Efficient Market," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 8(3). [Downloadable!]
  4. Huang, Bwo-Nung & Yang, Chin W, 1995. "The Fractal Structure in Multinational Stock Returns," Applied Economics Letters, Taylor and Francis Journals, vol. 2(3), pages 67-71, March. [Downloadable!] (restricted)
  5. Liu, T & Granger, C W J & Heller, W P, 1992. "Using the Correlation Exponent to Decide whether an Economic Series is Chaotic," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S25-39, Suppl. De. [Downloadable!] (restricted)
  6. Cass, David & Shell, Karl, 1983. "Do Sunspots Matter?," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 193-227, April. [Downloadable!] (restricted)
  7. Cal Muckley, 2004. "Empirical asset return distributions: is chaos the culprit? ," Applied Economics Letters, Taylor and Francis Journals, vol. 11(2), pages 81-86, February. [Downloadable!] (restricted)
  8. Fabrizio Lillo & J. Doyne Farmer, 2003. "The long memory of the efficient market," Quantitative Finance Papers cond-mat/0311053, arXiv.org, revised Jul 2004. [Downloadable!]
  9. Henry, Olan T, 2002. "Long Memory in Stock Returns: Some International Evidence," Applied Financial Economics, Taylor and Francis Journals, vol. 12(10), pages 725-29, October. [Downloadable!] (restricted)
  10. Hiemstra, Craig & Jones, Jonathan D., 1997. "Another look at long memory in common stock returns," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 373-401, December. [Downloadable!] (restricted)
  11. Christopher F. Baum & John Barkoulas, 1996. "Long Term Dependence in Stock Returns," Boston College Working Papers in Economics 314., Boston College Department of Economics. [Downloadable!]
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