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Multifractional Properties Of Stock Indices Decomposed By Filtering Their Pointwise Hölder Regularity

Author

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  • S. BIANCHI

    (Faculty of Economics, University of Cassino, Italy)

  • A. PIANESE

    (Faculty of Economics, University of Cassino, Italy)

Abstract

We propose a decomposition of financial time series into Gaussian subsequences characterized by a constant Hölder exponent. In (multi)fractal models this condition is equivalent to the subsequences themselves being stationarity. For the different subsequences, we study the scaling of the variance and the bias that is generated when the Hölder exponent is re-estimated using traditional estimators. The results achieved by both analyses are shown to be strongly consistent with the assumption that the price process can be modeled by the multifractional Brownian motion, a nonstationary process whose Hölder regularity changes from point to point.

Suggested Citation

  • S. Bianchi & A. Pianese, 2008. "Multifractional Properties Of Stock Indices Decomposed By Filtering Their Pointwise Hölder Regularity," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 567-595.
    • Handle: RePEc:wsi:ijtafx:v:11:y:2008:i:06:n:s0219024908004932
    DOI: 10.1142/S0219024908004932
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    Citations

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    Cited by:

    1. Angelini, Daniele & Bianchi, Sergio, 2023. "Nonlinear biases in the roughness of a Fractional Stochastic Regularity Model," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    2. Peng, Qidi & Zhao, Ran, 2018. "A general class of multifractional processes and stock price informativeness," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 248-267.
    3. Bianchi, Sergio & Pianese, Augusto, 2018. "Time-varying Hurst–Hölder exponents and the dynamics of (in)efficiency in stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 64-75.

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