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Multiscale multifractal DCCA and complexity behaviors of return intervals for Potts price model

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  • Wang, Jie
  • Wang, Jun
  • Stanley, H. Eugene

Abstract

To investigate the characteristics of extreme events in financial markets and the corresponding return intervals among these events, we use a Potts dynamic system to construct a random financial time series model of the attitudes of market traders. We use multiscale multifractal detrended cross-correlation analysis (MM-DCCA) and Lempel–Ziv complexity (LZC) perform numerical research of the return intervals for two significant China’s stock market indices and for the proposed model. The new MM-DCCA method is based on the Hurst surface and provides more interpretable cross-correlations of the dynamic mechanism between different return interval series. We scale the LZC method with different exponents to illustrate the complexity of return intervals in different scales. Empirical studies indicate that the proposed return intervals from the Potts system and the real stock market indices hold similar statistical properties.

Suggested Citation

  • Wang, Jie & Wang, Jun & Stanley, H. Eugene, 2018. "Multiscale multifractal DCCA and complexity behaviors of return intervals for Potts price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 889-902.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:889-902
    DOI: 10.1016/j.physa.2017.11.019
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    Citations

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    Cited by:

    1. Li, Shuping & Lu, Xinsheng & Liu, Xinghua, 2020. "Dynamic relationship between Chinese RMB exchange rate index and market anxiety: A new perspective based on MF-DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Jia, Linlu & Ke, Jinchuan & Wang, Jun, 2019. "Volatility aggregation intensity energy futures series on stochastic finite-range exclusion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 370-383.
    3. Zhong, Lin-Feng & Liu, Quan-Hui & Wang, Wei & Cai, Shi-Min, 2018. "Comprehensive influence of local and global characteristics on identifying the influential nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 78-84.
    4. Wang, Jie & Wang, Jun, 2020. "Cross-correlation complexity and synchronization of the financial time series on Potts dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Lahmiri, Salim & Bekiros, Stelios & Avdoulas, Christos, 2018. "Time-dependent complexity measurement of causality in international equity markets: A spatial approach," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 215-219.
    6. Zhong, Lin-Feng & Shang, Ming-Sheng & Chen, Xiao-Long & Cai, Shi-Ming, 2018. "Identifying the influential nodes via eigen-centrality from the differences and similarities of structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 77-82.
    7. Kristjanpoller, Werner & Minutolo, Marcel C., 2021. "Asymmetric multi-fractal cross-correlations of the price of electricity in the US with crude oil and the natural gas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    8. Wang, Yiduan & Zheng, Shenzhou & Zhang, Wei & Wang, Guochao & Wang, Jun, 2018. "Fuzzy entropy complexity and multifractal behavior of statistical physics financial dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 486-498.
    9. Stan, Cristina & Marmureanu, Luminita & Marin, Cristina & Cristescu, Constantin P., 2020. "Investigation of multifractal cross-correlation surfaces of Hurst exponents for some atmospheric pollutants," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    10. Zhao, Shangmei & Chen, Xinyi & Zhang, Junhuan, 2019. "The systemic risk of China’s stock market during the crashes in 2008 and 2015," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 161-177.

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