Mattarocci, Gianluca (2006): Market characteristics and chaos dynamics in stock markets: an international comparison.

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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.
Item Type:  MPRA Paper 

Institution:  University of Rome Tor Vergata  Sefemeq department 
Original Title:  Market characteristics and chaos dynamics in stock markets: an international comparison 
Language:  English 
Keywords:  Chaos; fractal dimension; R/S analysis and market characteristics 
Subjects:  D  Microeconomics > D4  Market Structure, Pricing, and Design > D49  Other G  Financial Economics > G1  General Financial Markets > G12  Asset Pricing ; Trading Volume ; Bond Interest Rates C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General F  International Economics > F3  International Finance > F37  International Finance Forecasting and Simulation: Models and Applications 
Item ID:  4296 
Depositing User:  Gianluca Mattarocci 
Date Deposited:  31 Jul 2007 
Last Modified:  26 Sep 2019 19:35 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/4296 