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

George Tzagkarakis*, Thomas Dionysopoulos, Alin Achim

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

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.

Original languageEnglish
Pages (from-to)75-96
Number of pages22
JournalStudies in Nonlinear Dynamics and Econometrics
Volume20
Issue number1
Early online date18 Jun 2015
DOIs
Publication statusPublished - 1 Feb 2016

Keywords

  • alpha-stable distributions
  • MAP denoising
  • recurrence quantification analysis
  • switching volatility regimes
  • wavelet decomposition

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