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A Way To Determine Chaotic Behaviour In Romanian Stock Market

Author

Listed:
  • Emilian Lucian NEACSU

    (West University of Timisoara, Faculty of Economics and Business Administration, Timisoara, Romania)

  • Marcela Daniela TODONI

    (West University of Timisoara, Faculty of Economics and Business Administration, Timisoara, Romania)

Abstract

It is difficult to distinguish between multiple random shocks and endogenous informational inflow in nonlinear systems which show complex dynamics. For this reason, we run the chaos tests to investigate the presence of chaotic phenomena using: nonlinearity tests, Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA). In this paper, we compute the Hurst Exponent using R/S analysis on Romanian capital market for a time span between 2005 - 2014 daily data. Substantial changes of Hurst Exponent behaviour in the current period compared to the previous one may be seen as structural break points in the series. The goal of this paper is to determine time series chaotic behaviour in order to highlight the efficiency levels of CEE markets. Also, we aim to investigate the changes in drifting dynamical systems, to examine the recurring patterns – the most important features of complex systems and to admire the "simple beauty of the complexity".

Suggested Citation

  • Emilian Lucian NEACSU & Marcela Daniela TODONI, 2014. "A Way To Determine Chaotic Behaviour In Romanian Stock Market," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 14, pages 207-214, December.
    • Handle: RePEc:aic:revebs:y:2014:d:14:neacsue
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    References listed on IDEAS

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    More about this item

    Keywords

    Hurst exponent; chaotic behaviour; structural breaks; recurrence analysis;
    All these keywords.

    JEL classification:

    • B59 - Schools of Economic Thought and Methodology - - Current Heterodox Approaches - - - Other
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G01 - Financial Economics - - General - - - Financial Crises
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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