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Time-dependent complexity measurement of causality in international equity markets: A spatial approach

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  • Lahmiri, Salim
  • Bekiros, Stelios
  • Avdoulas, Christos
Abstract
A nonlinear temporal complexity approach is proposed in order to properly model the evolution of randomness, self-similarity and information transmission for thirty-four international stock markets, grouped into four major geographical segments: America, Europe, Asia and Oceania. The causality between each type of time-dependent measures is investigated to assess the state system flows across all geographic segments. The empirical results show that self-similarity is vastly transmitted between financial markets. Moreover, significant emissions of entropy and self-similarity are found between America and Europe. Informational flows are observed only between Europe and Asia, and Europe and Oceania. Our findings may have important implications for portfolio management based on the spatial dimension of spillovers of stochasticity, self-similarity and system state informational content for world stock markets. These results would not have emerged by means of standard econometric approaches of causality investigation in financial returns.

Suggested Citation

  • Lahmiri, Salim & Bekiros, Stelios & Avdoulas, Christos, 2018. "Time-dependent complexity measurement of causality in international equity markets: A spatial approach," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 215-219.
  • Handle: RePEc:eee:chsofr:v:116:y:2018:i:c:p:215-219
    DOI: 10.1016/j.chaos.2018.09.030
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    References listed on IDEAS

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    1. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
    2. Ludwig O. Dittrich & Pavel Srbek, 2018. "Long-Range Dependence in Daily Return Stock Market Series," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 24(3), pages 285-286, August.
    3. Wang, Jie & Wang, Jun & Stanley, H. Eugene, 2018. "Multiscale multifractal DCCA and complexity behaviors of return intervals for Potts price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 889-902.
    4. Stelios Bekiros, 2014. "Timescale Analysis with an Entropy-Based Shift-Invariant Discrete Wavelet Transform," Computational Economics, Springer;Society for Computational Economics, vol. 44(2), pages 231-251, August.
    5. Giglio, Ricardo & Matsushita, Raul & Figueiredo, Annibal & Gleria, Iram & Da Silva, Sergio, 2008. "Algorithmic complexity theory and the relative efficiency of financial markets," MPRA Paper 8704, University Library of Munich, Germany.
    6. Lahmiri, Salim & Bekiros, Stelios, 2018. "Chaos, randomness and multi-fractality in Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 28-34.
    7. Benjamin Rainer Auer, 2018. "Are standard asset pricing factors long-range dependent?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(1), pages 66-88, January.
    8. Zhang, Yali & Wang, Jun, 2017. "Nonlinear complexity of random visibility graph and Lempel-Ziv on multitype range-intensity interacting financial dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 741-756.
    9. Giglio, Ricardo & Matsushita, Raul & Figueiredo, Annibal & Gleria, Iram & Da Silva, Sergio, 2008. "Algorithmic complexity theory and the relative efficiency of financial markets - Updated," MPRA Paper 11150, University Library of Munich, Germany.
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    5. Lahmiri, Salim & Bekiros, Stelios, 2020. "Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Xavier Brouty & Matthieu Garcin, 2022. "A statistical test of market efficiency based on information theory," Working Papers hal-03760478, HAL.
    7. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
    8. Xavier Brouty & Matthieu Garcin, 2022. "A statistical test of market efficiency based on information theory," Papers 2208.11976, arXiv.org.
    9. Brouty, Xavier & Garcin, Matthieu, 2024. "Fractal properties, information theory, and market efficiency," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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