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Modeling stock prices by multifractional Brownian motion: an improved estimation of the pointwise regularity

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  • S. Bianchi
  • A. Pantanella
  • A. Pianese

Abstract

This paper deals with the problem of estimating the pointwise regularity of multifractional Brownian motion, assumed as a model of stock price dynamics. We (a) correct the shifting bias affecting a class of absolute moment-based estimators and (b) build a data-driven algorithm in order to dynamically check the local Gaussianity of the process. The estimation is therefore performed for three stock indices: the Dow Jones Industrial Average, the FTSE 100 and the Nikkei 225. Our findings show that, after the correction, the pointwise regularity fluctuates around 1/2 (the sole value consistent with the absence of arbitrage), but significant deviations are also observed.

Suggested Citation

  • S. Bianchi & A. Pantanella & A. Pianese, 2013. "Modeling stock prices by multifractional Brownian motion: an improved estimation of the pointwise regularity," Quantitative Finance, Taylor & Francis Journals, vol. 13(8), pages 1317-1330, July.
    • Handle: RePEc:taf:quantf:v:13:y:2013:i:8:p:1317-1330
    DOI: 10.1080/14697688.2011.594080
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    Citations

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    Cited by:

    1. Sergio Bianchi & Massimiliano Frezza, 2018. "Liquidity, Efficiency and the 2007-2008 Global Financial Crisis," Annals of Economics and Finance, Society for AEF, vol. 19(2), pages 375-404, November.
    2. Britta Förster & Bernd Hayo, 2018. "Monetary and Fiscal Policy in Times of Crisis: A New Keynesian Perspective in Continuous Time," Manchester School, University of Manchester, vol. 86(1), pages 21-48, January.
    3. Rosella Castellano & Roy Cerqueti & Giulia Rotundo, 2020. "Exploring the financial risk of a temperature index: a fractional integrated approach," Annals of Operations Research, Springer, vol. 284(1), pages 225-242, January.
    4. Villena, Marcelo J. & Araneda, Axel A., 2024. "On sectoral market efficiency," Finance Research Letters, Elsevier, vol. 61(C).
    5. Angelini, Daniele & Bianchi, Sergio, 2023. "Nonlinear biases in the roughness of a Fractional Stochastic Regularity Model," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    6. Tsai, Yi-Cheng & Lei, Chin-Laung & Cheung, William & Wu, Chung-Shu & Ho, Jan-Ming & Wang, Chuan-Ju, 2018. "Exploring the Persistent Behavior of Financial Markets," Finance Research Letters, Elsevier, vol. 24(C), pages 199-220.
    7. Frezza, Massimiliano & Bianchi, Sergio & Pianese, Augusto, 2021. "Fractal analysis of market (in)efficiency during the COVID-19," Finance Research Letters, Elsevier, vol. 38(C).
    8. Gerlich, Nikolas & Rostek, Stefan, 2015. "Estimating serial correlation and self-similarity in financial time series—A diversification approach with applications to high frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 84-98.
    9. 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.
    10. Axel A. Araneda, 2023. "A multifractional option pricing formula," Papers 2303.16314, arXiv.org, revised Jun 2024.
    11. Zhang, H.S. & Shen, X.Y. & Huang, J.P., 2016. "Pattern of trends in stock markets as revealed by the renormalization method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 340-346.
    12. Roy Cerqueti & Giulia Rotundo, 2015. "A review of aggregation techniques for agent-based models: understanding the presence of long-term memory," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1693-1717, July.
    13. Sergio Bianchi & Augusto Pianese & Massimiliano Frezza, 2020. "A distribution‐based method to gauge market liquidity through scale invariance between investment horizons," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(5), pages 809-824, September.
    14. Ayache, Antoine & Bouly, Florent, 2022. "Moving average Multifractional Processes with Random Exponent: Lower bounds for local oscillations," Stochastic Processes and their Applications, Elsevier, vol. 146(C), pages 143-163.
    15. Sixian Jin & Qidi Peng & Henry Schellhorn, 2018. "Estimation of the pointwise Hölder exponent of hidden multifractional Brownian motion using wavelet coefficients," Statistical Inference for Stochastic Processes, Springer, vol. 21(1), pages 113-140, April.
    16. 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.
    17. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2015. "Time-dependent scaling patterns in high frequency financial data," Papers 1508.07428, arXiv.org, revised Dec 2015.
    18. Massimiliano Frezza & Sergio Bianchi & Augusto Pianese, 2022. "Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process," Computational Management Science, Springer, vol. 19(1), pages 99-132, January.

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