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Chaotic behavior in financial market volatility

Author

Listed:
  • Houda Litimi
  • Ahmed Bensaida
  • Lotfi Belkacem
  • Oussama Abdallah

    (LIRIS - Laboratoire interdisciplinaire de recherche en innovations sociétales - UR2 - Université de Rennes 2)

Abstract

The study of chaotic dynamics in financial time series suffers from the nature of the collected data, which is both finite and noisy. Moreover, researchers have become less enthusiastic since a large body of the literature found no evidence of chaotic dynamics in financial returns. In this paper, we present a robust method for the detection of chaos based on the Lyapunov exponent, which is consistent even for noisy and finite scalar time series. To revitalize the debate on nonlinear dynamics in financial markets, we show that the volatility is chaotic. Applications carried out on eight major daily volatility indexes support the presence of low-level chaos. Further, our out-of-sample analysis demonstrates the superiority of neural networks, compared with other chaotic maps, in the forecasting of market volatility.

Suggested Citation

  • Houda Litimi & Ahmed Bensaida & Lotfi Belkacem & Oussama Abdallah, 2019. "Chaotic behavior in financial market volatility," Post-Print hal-02869485, HAL.
    • Handle: RePEc:hal:journl:hal-02869485
    DOI: 10.21314/JOR.2018.400
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    Cited by:

    1. Omane-Adjepong, Maurice & Alagidede, Imhotep Paul, 2020. "High- and low-level chaos in the time and frequency market returns of leading cryptocurrencies and emerging assets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    2. Bildirici, Melike E. & Sonustun, Bahri, 2021. "Chaotic behavior in gold, silver, copper and bitcoin prices," Resources Policy, Elsevier, vol. 74(C).

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