Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: detecting switching volatility regimes
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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.
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Supplemental Material:
The online version of this article (DOI: 10.1515/snde-2014-0102) offers supplementary material, available to authorized users.
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Artikel in diesem Heft
- Frontmatter
- Are US real house prices stationary? New evidence from univariate and panel data
- Probabilistic and statistical properties of moment variations and their use in inference and estimation based on high frequency return data
- Outliers and persistence in threshold autoregressive processes
- Testing for long memory in the presence of non-linear deterministic trends with Chebyshev polynomials
- Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: detecting switching volatility regimes
- Selecting the tuning parameter of the ℓ1 trend filter
Artikel in diesem Heft
- Frontmatter
- Are US real house prices stationary? New evidence from univariate and panel data
- Probabilistic and statistical properties of moment variations and their use in inference and estimation based on high frequency return data
- Outliers and persistence in threshold autoregressive processes
- Testing for long memory in the presence of non-linear deterministic trends with Chebyshev polynomials
- Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: detecting switching volatility regimes
- Selecting the tuning parameter of the ℓ1 trend filter
