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

Tzagkarakis George (), Dionysopoulos Thomas and Achim Alin
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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

Studies in Nonlinear Dynamics & Econometrics, 2016, vol. 20, issue 1, 75-96

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.

Keywords: alpha-stable distributions; MAP denoising; recurrence quantification analysis; switching volatility regimes; wavelet decomposition (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/snde-2014-0102

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