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Multifractal Diffusion Entropy Analysis: Optimal Bin Width of Probability Histograms

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  • Petr Jizba
  • Jan Korbel

Abstract

In the framework of Multifractal Diffusion Entropy Analysis we propose a method for choosing an optimal bin-width in histograms generated from underlying probability distributions of interest. The method presented uses techniques of R\'{e}nyi's entropy and the mean squared error analysis to discuss the conditions under which the error in the multifractal spectrum estimation is minimal. We illustrate the utility of our approach by focusing on a scaling behavior of financial time series. In particular, we analyze the S&P500 stock index as sampled at a daily rate in the time period 1950-2013. In order to demonstrate a strength of the method proposed we compare the multifractal $\delta$-spectrum for various bin-widths and show the robustness of the method, especially for large values of $q$. For such values, other methods in use, e.g., those based on moment estimation, tend to fail for heavy-tailed data or data with long correlations. Connection between the $\delta$-spectrum and R\'{e}nyi's $q$ parameter is also discussed and elucidated on a simple example of multiscale time series.

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  • Petr Jizba & Jan Korbel, 2014. "Multifractal Diffusion Entropy Analysis: Optimal Bin Width of Probability Histograms," Papers 1401.3316, arXiv.org, revised Mar 2014.
    • Handle: RePEc:arx:papers:1401.3316
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    1. Hall, Peter & Wand, Matthew P., 1988. "Minimizing L1 distance in nonparametric density estimation," Journal of Multivariate Analysis, Elsevier, vol. 26(1), pages 59-88, July.
    2. Kim, Kyungsik & Yoon, Seong-Min, 2004. "Multifractal features of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 272-278.
    3. Park, Joongwoo Brian & Won Lee, Jeong & Yang, Jae-Suk & Jo, Hang-Hyun & Moon, Hie-Tae, 2007. "Complexity analysis of the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 179-187.
    4. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    5. Morales, Raffaello & Di Matteo, T. & Gramatica, Ruggero & Aste, Tomaso, 2012. "Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3180-3189.
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    Cited by:

    1. Będowska-Sójka, Barbara & Kliber, Agata, 2021. "Information content of liquidity and volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    2. Mahmoodi, Korosh & West, Bruce J. & Grigolini, Paolo, 2020. "On the dynamical foundation of multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    3. Petr Jizba & Jan Korbel, 2016. "Techniques for multifractal spectrum estimation in financial time series," Papers 1610.07028, arXiv.org.

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