Empirical volatility analysis: feature detection and signal extraction with function dictionaries
AbstractWe aim to investigate the potential usefulness of wavelets for representing and decomposing financial volatility processes. Our strategy relies on the empirical analysis of high-frequency intradaily stock index returns by using adaptive signal-processing techniques which exploit the approximation and computational power of wavelet transforms. We first deal with data pre-processing and pre-smoothing, before addressing the statistical model building stage. We thus introduce a flexible parametric model that yields an effective empirical volatility analysis tool, capable of handling and detecting latent periodicities, and consequently delivering more accurate signal estimates. We extract the structure of volatility through the information content of projected signals obtained by representing and approximating the observed returns with special function dictionaries that may significantly contribute to reduce the risk that standard volatility models might fail to achieve meaningful statistical inference.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 319 (2003)
Issue (Month): C ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
High-frequency volatility; wavelets; Signal extraction; SureShrink estimators; Feature detection; Matching pursuit;
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- Capobianco, Enrico, 2008. "Kernel methods and flexible inference for complex stochastic dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4077-4098.
- Antonios Antoniou & Constantinos E. Vorlow, 2004. "Price Clustering and Discreteness: Is there Chaos behind the Noise?," Papers cond-mat/0407471, arXiv.org.
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