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Option pricing from wavelet-filtered financial series

Author

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  • de Almeida, V.T.X.
  • Moriconi, L.

Abstract

We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most (≃99.6%) of the wavelet coefficients can be neglected for the purpose of option premium evaluation. The relevance of the hugely compressed information provided by low-pass wavelet-filtering is related to the fact that the non-gaussian statistical structure of the original financial time series is essentially preserved for expiration times which are larger than just one trading day.

Suggested Citation

  • de Almeida, V.T.X. & Moriconi, L., 2012. "Option pricing from wavelet-filtered financial series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4850-4854.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:20:p:4850-4854
    DOI: 10.1016/j.physa.2012.05.030
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    References listed on IDEAS

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    1. Wilmott,Paul & Howison,Sam & Dewynne,Jeff, 1995. "The Mathematics of Financial Derivatives," Cambridge Books, Cambridge University Press, number 9780521497893, September.
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    Cited by:

    1. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2015. "Identifying the multiscale impacts of crude oil price shocks on the stock market in China at the sector level," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 13-24.
    2. 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.

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