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Etude de la dynamique non-linéaire des rentabilités de la bourse de Casablanca
[Study of the returns nonlinear dynamics of the Casablanca stock exchange]

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  • RIANE, Nizare

Abstract

The preponderance of the linear approach in the stock market modeling is the result of the Frisch-Slutsky paradigm which implies that the market can only converge to an equilibrium point or diverge, according to a monotonic or oscillatory trajectory. Moreover, this description of reality is insufficient, first by his inability to describe the fluctuations that tend to persist and market anomalies, second by the weakness of the linear statistical tests facing more complex processes. In this paper, we examine the existence of a non-linear dynamics that govern the evolution of the MASI Index. The analysis uses the concepts of Lyapunov exponents, correlation dimension and other tools to determine the nature of the underlying process. The results provide evidence of a non-linear process, but the determinism remains contested.

Suggested Citation

  • RIANE, Nizare, 2014. "Etude de la dynamique non-linéaire des rentabilités de la bourse de Casablanca [Study of the returns nonlinear dynamics of the Casablanca stock exchange]," MPRA Paper 61957, University Library of Munich, Germany, revised 06 Feb 2015.
    • Handle: RePEc:pra:mprapa:61957
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    References listed on IDEAS

    as
    1. Nelson, Charles R & Piger, Jeremy & Zivot, Eric, 2001. "Markov Regime Switching and Unit-Root Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 404-415, October.
    2. Isabelle Girerd-Potin & Ollivier Taramasco, 1994. "Les rentabilités à la bourse de Paris sont-elles chaotiques ?," Revue Économique, Programme National Persée, vol. 45(2), pages 215-238.
    3. Bonache, Adrien, 2008. "Les ventes de produits innovants à la mode sont-elles chaotiques? Le cas des ventes de Game Boy au Japon [Are innovative and fashion goods sales chaotic? The case of Game Boy sales in Japan]," MPRA Paper 12964, University Library of Munich, Germany.
    4. Weihs, Claus & Busse, Anja M., 2004. "Lyapunov exponent for stochastic time series," Technical Reports 2004,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Strozzi, Fernanda & Zaldı́var, José-Manuel & Zbilut, Joseph P, 2002. "Application of nonlinear time series analysis techniques to high-frequency currency exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 520-538.
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    Cited by:

    1. Daniele TUEDEM WAFFO & Jules Roger FEUDJO & dagobert NGONGANG, 2016. "Mécanismes de gestion du risque de crédit ex ante et performance globale des EMF camerounais," Journal of Academic Finance, RED research unit, university of Gabes, Tunisia, vol. 7(2), pages 53-69, November.
    2. Monia ANTAR, 2016. "Autosimilarité et mémoire longue : Les rendements des indices boursiers tunisiens sont-ils chaotiques ?," Journal of Academic Finance, RED research unit, university of Gabes, Tunisia, vol. 7(2), pages 1-32, November.

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    More about this item

    Keywords

    Chaos; attractor; nonlinearity; determinism; Lyapunov exponent; correlation dimension.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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