Hammerstein system represention of financial volatility processes

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18 Scopus citations

Abstract

We show new modeling aspects of stock return volatility processes, by first representing them through Hammerstein Systems, and by then approximating the observed and transformed dynamics with wavelet-based atomic dictionaries. We thus propose an hybrid statistical methodology for volatility approximation and non-parametric estimation, and aim to use the information embedded in a bank of volatility sources obtained by decomposing the observed signal with multiresolution techniques. Scale dependent information refers both to market activity inherent to different temporally aggregated trading horizons, and to a variable degree of sparsity in representing the signal. A decomposition of the expansion coefficients in least dependent coordinates is then implemented through Independent Component Analysis. Based on the described steps, the features of volatility can be more effectively detected through global and greedy algorithms.

Original languageEnglish (US)
Pages (from-to)201-211
Number of pages11
JournalEuropean Physical Journal B
Volume27
Issue number2
DOIs
StatePublished - May 2 2002
Externally publishedYes

Keywords

  • 02.50.Tt Inference methods
  • 02.60.Gf Algorithms for functional approximation
  • 05.45.Tp Time series analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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