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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

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

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    Fama
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    Gençay
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    Roll
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    Chambers
  • Use of Recurrence Quantification Analysis in Economic Time Series Complex Windows
    Zbilut
  • Multi - scale Tests for Serial Correlation of
    Gençay
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    Gençay
  • Wavelet Tour of Signal Processing The Sparse Way rd ed Academic ISBN
    Mallat
  • Comovement of Stock Market Returns Wavelet of Empirical
    Nunes
  • Systematic Risk of and
    Gençay
  • Understanding Variance Bias Estimation
    Bruce
  • Assessing Nonlinear Structures in Real Exchange Rates Using Recurrence Plot Strategies
    Belaire
  • Application to Financial Markets The of
    Hsieh
  • Embedding Theory Introduction and Applications to Time Series Analysis Modelling and Forecasting Financial Data Techniques of Nonlinear volume Media New York Kluwer Academic Publishers
    Strozzi
  • Redundant Multiscale Transforms and their Application for Morphological Component Separation in Imaging and
    Starck
  • The Variation of Certain Speculative Prices The of
    Mandelbrot
  • Influence of Observational Noise on the Recurrence Quantification
    Thiel
  • Measuring Volatility in the Nordic Spot Electricity Market Using Recurrence Quantification The European -
    Strozzi
  • Comovement of Stock Market Returns Wavelet of Empirical
    Nunes
  • The Stationary Wavelet Transform and Some Statistical Applications in volume New York
    Nason
  • Adapting to Unknown Smoothness via Wavelet Shrinkage of the
    Donoho
  • Ideal Spatial Adaptation via Wavelet Shrinkage
    Donoho
  • Kennel Determining Embedding Dimension for Phase - space Reconstruction Using a Geometrical Construction A
    Brown
  • Detection with Wavelets for High - frequency Financial Time Series
    Gençay
  • and Delays as Derived from Quantification of Recurrence Plots
    Zbilut
  • Schinkel Selection of Recurrence Threshold for Signal Detection
    Dimigen
  • Multi scale Volatility Analysis for High - Frequency Financial Data of the
    Fan
  • Exploring Exchange Rate Returns at Different Time
    Nekhili
  • Influence of Observational Noise on the Recurrence Quantification
    Thiel
  • a An Introduction to Wavelets and Other Filtering Methods in and San CA Academic
    Gençay
  • Multi - period Hedge Ratios for a Multi - asset Portfolio when Accounting for Returns Co - movement of
    Fernandez
  • Schinkel Selection of Recurrence Threshold for Signal Detection
    Dimigen
  • GARCH Option Pricing Model withα - stable Innovations
    Menn
  • of
    Sauer
  • Financial Market Models with Lévy Processes and Time - varying Volatility of
    Kim
  • Exploring Exchange Rate Returns at Different Time
    Nekhili
  • Observation of Deterministic Chaos in Financial Time Series by Recurrence Plots Can One Control Chaotic Economy
    Holyst
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    Aguiar