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Wavelet Transforms For The Statistical Analysis Of Returns Generating Stochastic Processes

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  • ENRICO CAPOBIANCO

    (CWI, Kruislaan 413, 1098 SJ Amsterdam (NL), The Netherlands)

Abstract

We study high frequency Nikkei stock index series and investigate what certain wavelet transforms suggest in terms of volatility features underlying the observed returns process. Several wavelet transforms are applied for exploratory data analysis. One of the scopes is to use wavelets as a pre-processing smoothing tool so to de-noise the data; we believe that this procedure may help in identifying, estimating and predicting the latent volatility. Evidence is shown on how a non-parametric statistical procedure such as wavelets may be useful for improving the generalization power of GARCH models when applied to de-noised returns.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijtafx:v:04:y:2001:i:03:n:s0219024901001097
    DOI: 10.1142/S0219024901001097
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    Citations

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    Cited by:

    1. Jammazi, Rania & Lahiani, Amine & Nguyen, Duc Khuong, 2015. "A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 173-187.
    2. 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.
    3. Antonios Antoniou & Constantinos E. Vorlow, 2004. "Price Clustering and Discreteness: Is there Chaos behind the Noise?," Papers cond-mat/0407471, arXiv.org.
    4. Liu, Xiaoquan & Cao, Yi & Ma, Chenghu & Shen, Liya, 2019. "Wavelet-based option pricing: An empirical study," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1132-1142.
    5. Ye, Wuyi & Luo, Kebing & Liu, Xiaoquan, 2017. "Time-varying quantile association regression model with applications to financial contagion and VaR," European Journal of Operational Research, Elsevier, vol. 256(3), pages 1015-1028.
    6. Antoniou, Antonios & Vorlow, Constantinos E., 2004. "Recurrence quantification analysis of wavelet pre-filtered index returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 257-262.
    7. Wen, Shaobo & An, Haizhong & Huang, Shupei & Liu, Xueyong, 2019. "Dynamic impact of China's stock market on the international commodity market," Resources Policy, Elsevier, vol. 61(C), pages 564-571.
    8. Haven, Emmanuel & Liu, Xiaoquan & Shen, Liya, 2012. "De-noising option prices with the wavelet method," European Journal of Operational Research, Elsevier, vol. 222(1), pages 104-112.
    9. Antoniou, Antonios & Vorlow, Constantinos E., 2005. "Price clustering and discreteness: is there chaos behind the noise?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 389-403.

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