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On the interplay between multiscaling and stocks dependence

Author

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  • R. J. Buonocore
  • G. Brandi
  • R. N. Mantegna
  • T. Di Matteo

Abstract

We find a nonlinear dependence between an indicator of the degree of multiscaling of log-price time series of a stock and the average correlation of the stock with respect to the other stocks traded in the same market. This result is a robust stylized fact holding for different financial markets. We investigate this result conditional on the stocks' capitalization and on the kurtosis of stocks' log-returns in order to search for possible confounding effects. We show that a linear dependence with the logarithm of the capitalization and the logarithm of kurtosis does not explain the observed stylized fact, which we interpret as being originated from a deeper relationship.

Suggested Citation

  • R. J. Buonocore & G. Brandi & R. N. Mantegna & T. Di Matteo, 2018. "On the interplay between multiscaling and stocks dependence," Papers 1802.01113, arXiv.org, revised Mar 2019.
    • Handle: RePEc:arx:papers:1802.01113
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    Cited by:

    1. Giuseppe Brandi & T. Di Matteo, 2022. "Multiscaling and rough volatility: an empirical investigation," Papers 2201.10466, arXiv.org.
    2. Antoniades, I.P. & Karakatsanis, L.P. & Pavlos, E.G., 2021. "Dynamical characteristics of global stock markets based on time dependent Tsallis non-extensive statistics and generalized Hurst exponents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    3. Brandi, Giuseppe & Di Matteo, T., 2022. "Multiscaling and rough volatility: An empirical investigation," International Review of Financial Analysis, Elsevier, vol. 84(C).
    4. Bariviera, Aurelio F., 2021. "One model is not enough: Heterogeneity in cryptocurrencies’ multifractal profiles," Finance Research Letters, Elsevier, vol. 39(C).
    5. Antoniades, I.P. & Brandi, Giuseppe & Magafas, L. & Di Matteo, T., 2021. "The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    6. Ioannis P. Antoniades & Giuseppe Brandi & L. G. Magafas & T. Di Matteo, 2020. "The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool," Papers 2010.08890, arXiv.org, revised Dec 2020.
    7. Arias-Calluari, Karina & Najafi, Morteza. N. & Harré, Michael S. & Tang, Yaoyue & Alonso-Marroquin, Fernando, 2022. "Testing stationarity of the detrended price return in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    8. Wang, Yi & Sun, Qi & Zhang, Zilu & Chen, Liqing, 2022. "A risk measure of the stock market that is based on multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    9. Villena, Marcelo J. & Araneda, Axel A., 2024. "On sectoral market efficiency," Finance Research Letters, Elsevier, vol. 61(C).
    10. Un, Kuok Sin & Ausloos, Marcel, 2022. "Equity premium prediction: Taking into account the role of long, even asymmetric, swings in stock market behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    11. Ioannis P. Antoniades & Leonidas P. Karakatsanis & Evgenios G. Pavlos, 2020. "Dynamical Characteristics of Global Stock Markets Based on Time Dependent Tsallis Non-Extensive Statistics and Generalized Hurst Exponents," Papers 2012.06856, arXiv.org, revised Apr 2021.
    12. Shen, Na & Chen, Jiayi, 2023. "Asymmetric multifractal spectrum distribution based on detrending moving average cross-correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).

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