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Empirical volatility analysis: feature detection and signal extraction with function dictionaries

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  • Capobianco, Enrico

Abstract

We 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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  • Capobianco, Enrico, 2003. "Empirical volatility analysis: feature detection and signal extraction with function dictionaries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 495-518.
  • Handle: RePEc:eee:phsmap:v:319:y:2003:i:c:p:495-518
    DOI: 10.1016/S0378-4371(02)01369-9
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    References listed on IDEAS

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    7. Enrico Capobianco, 2001. "Wavelet Transforms For The Statistical Analysis Of Returns Generating Stochastic Processes," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 511-534.
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    Cited by:

    1. Antonios Antoniou & Constantinos E. Vorlow, 2004. "Price Clustering and Discreteness: Is there Chaos behind the Noise?," Papers cond-mat/0407471, arXiv.org.
    2. Kaijian He & Kin Keung Lai & Guocheng Xiang, 2012. "Portfolio Value at Risk Estimate for Crude Oil Markets: A Multivariate Wavelet Denoising Approach," Energies, MDPI, vol. 5(4), pages 1-26, April.
    3. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    4. 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.

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