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Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: detecting switching volatility regimes

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  • Tzagkarakis George

    (EONOS Investment Technologies, Paris, France)

  • Dionysopoulos Thomas

    (Avenir Finance Investment Managers, Paris, France AXIANTA Research, Nicosia, Cyprus)

  • Achim Alin

    (University of Bristol – Visual Information Lab, Bristol, UK)

Abstract

In this paper we propose an enhancement of recurrence quantification analysis (RQA) performance in extracting the underlying non-linear dynamics of market index returns, under the assumption of data corrupted by additive white Gaussian noise. More specifically, first we show that the statistical distribution of wavelet decompositions of distinct index returns is best fitted using members of the alpha-stable distributions family. Then, an efficient maximum a posteriori (MAP) estimator is applied on pairs of wavelet coefficients at adjacent levels to suppress the noise effect, prior to performing RQA. Quantitative and qualitative results on 22 future indices indicate an improved interpretation capability of RQA when applied on denoised data using our proposed approach, as opposed to previous methods based solely on a Gaussian assumption for the underlying statistics, in terms of extracting the underlying dynamical structure of index returns generating processes. Furthermore, our results reveal an increased accuracy of the proposed method in detecting switching volatility regimes, which is important for estimating the risk associated with a financial instrument.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sndecm:v:20:y:2016:i:1:p:75-96:n:6
    DOI: 10.1515/snde-2014-0102
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    References listed on IDEAS

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