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Effective decorrelation and space dimensionality reduction of multiscaling volatility

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  • Capobianco, Enrico

Abstract

We consider an approach for modeling non-stationary and non-Gaussian curves which has a natural impact on financial time series analysis due to the characteristic features of volatility processes. Provided that one can approximate the signal of interest, in this case stock index returns, with a greedy approximation scheme based on wavelet-like functions, an effective space dimensionality reduction of the problem can be found by a decomposition technique which selects the scales according to an energy-based optimization scheme and finds the most informative sources of the underlying multiscaling volatility process.

Suggested Citation

  • Capobianco, Enrico, 2004. "Effective decorrelation and space dimensionality reduction of multiscaling volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(1), pages 340-346.
    • Handle: RePEc:eee:phsmap:v:340:y:2004:i:1:p:340-346
    DOI: 10.1016/j.physa.2004.04.025
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    1. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    2. Enrico Capobianco, 2002. "Multiresolution approximation for volatility processes," Quantitative Finance, Taylor & Francis Journals, vol. 2(2), pages 91-110.
    3. Iain M. Johnstone & Bernard W. Silverman, 1997. "Wavelet Threshold Estimators for Data with Correlated Noise," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(2), pages 319-351.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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