Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

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

Download PDF
22 pages

Loading next page...
 
/lp/de-gruyter/recurrence-quantification-analysis-of-denoised-index-returns-via-alpha-RGKy0OUkKc

References (89)

Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
1081-1826
eISSN
1558-3708
DOI
10.1515/snde-2014-0102
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Studies in Nonlinear Dynamics & Econometrics – de Gruyter

Published: Feb 1, 2016

There are no references for this article.