Chaotic behavior in financial market volatility

Houda Litimi, Ahmed BenSaïda, Lotfi Belkacem and Oussama Abdallah
Additional contact information
Oussama Abdallah: LIRIS - Laboratoire interdisciplinaire de recherche en innovations sociétales - UR2 - Université de Rennes 2

Post-Print from HAL

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Published in Journal of Risk Finance, 2019, 21 (3), pp.27-53. ⟨10.21314/JOR.2018.400⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02869485

DOI: 10.21314/JOR.2018.400

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().