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Recurrence quantification analysis of wavelet pre-filtered index returns

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  • Antoniou, Antonios
  • Vorlow, Constantinos E.

Abstract

In this paper we investigate for the presence of non-stochastic, possibly nonlinear deterministic dynamical cycles in financial time series. Evidence of nonlinear dynamics is revealed in denoised daily stock market index returns for six countries by combining Recurrence Quantification Analysis (RQA: see Zbilut and Webber (J. Appl. Phys. 76(2) (1994) 965)) and wavelet filtering. Quantitative and qualitative results indicate that through wavelet pre-filtering we can obtain a clearer view of the underlying dynamical structure of returns generating processes. Our results also suggest the existence of high dimensional deterministic dynamics, unstable periodic orbits and chaos.

Suggested Citation

  • Antoniou, Antonios & Vorlow, Constantinos E., 2004. "Recurrence quantification analysis of wavelet pre-filtered index returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 257-262.
    • Handle: RePEc:eee:phsmap:v:344:y:2004:i:1:p:257-262
    DOI: 10.1016/j.physa.2004.06.128
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    References listed on IDEAS

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    1. Enrico Capobianco, 1999. "Statistical Analysis of Financial Volatility by Wavelet Shrinkage," Methodology and Computing in Applied Probability, Springer, vol. 1(4), pages 423-443, December.
    2. Enrico Capobianco, 2002. "Multiresolution approximation for volatility processes," Quantitative Finance, Taylor & Francis Journals, vol. 2(2), pages 91-110.
    3. Enrico Capobianco, 2001. "Wavelet Transforms For The Statistical Analysis Of Returns Generating Stochastic Processes," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 511-534.
    4. Chen Ping, 1996. "A Random Walk or Color Chaos on the Stock Market? Time-Frequency Analysis of S&P Indexes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(2), pages 1-19, July.
    5. Ramsey James B., 2002. "Wavelets in Economics and Finance: Past and Future," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(3), pages 1-29, November.
    6. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
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    Cited by:

    1. Tzagkarakis George & Dionysopoulos Thomas & Achim Alin, 2016. "Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: detecting switching volatility regimes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 75-96, February.
    2. Antonios Antoniou & Constantinos E. Vorlow, 2004. "Price Clustering and Discreteness: Is there Chaos behind the Noise?," Papers cond-mat/0407471, arXiv.org.
    3. Constantinos E. Vorlow, 2004. "Stock Price Clustering and Discreteness: The "Compass Rose" and Predictability," Papers cond-mat/0408013, arXiv.org.
    4. Alexandros Leontitsis & Constantinos E. Vorlow, 2005. "Accounting for outliers and calendar effects in surrogate simulations of stock return sequences," Papers physics/0504187, arXiv.org.
    5. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    6. Antoniou, Antonios & Vorlow, Constantinos E., 2005. "Price clustering and discreteness: is there chaos behind the noise?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 389-403.
    7. Leontitsis, Alexandros & Vorlow, Constantinos E., 2006. "Accounting for outliers and calendar effects in surrogate simulations of stock return sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(2), pages 522-530.

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