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Fluctuation behaviors of financial return volatility duration

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  • Niu, Hongli
  • Wang, Jun
  • Lu, Yunfan

Abstract

It is of significantly crucial to understand the return volatility of financial markets because it helps to quantify the investment risk, optimize the portfolio, and provide a key input of option pricing models. The characteristics of isolated high volatility events above certain threshold in price fluctuations and the distributions of return intervals between these events arouse great interest in financial research. In the present work, we introduce a new concept of daily return volatility duration, which is defined as the shortest passage time when the future volatility intensity is above or below the current volatility intensity (without predefining a threshold). The statistical properties of the daily return volatility durations for seven representative stock indices from the world financial markets are investigated. Some useful and interesting empirical results of these volatility duration series about the probability distributions, memory effects and multifractal properties are obtained. These results also show that the proposed stock volatility series analysis is a meaningful and beneficial trial.

Suggested Citation

  • Niu, Hongli & Wang, Jun & Lu, Yunfan, 2016. "Fluctuation behaviors of financial return volatility duration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 30-40.
    • Handle: RePEc:eee:phsmap:v:448:y:2016:i:c:p:30-40
    DOI: 10.1016/j.physa.2015.12.088
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    Cited by:

    1. Wang, Guochao & Zheng, Shenzhou & Wang, Jun, 2020. "Fluctuation and volatility dynamics of stochastic interacting energy futures price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. Zhang, Yali & Wang, Jun, 2017. "Nonlinear complexity of random visibility graph and Lempel-Ziv on multitype range-intensity interacting financial dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 741-756.
    3. Niu, Hongli & Wang, Weiqing & Zhang, Junhuan, 2019. "Recurrence duration statistics and time-dependent intrinsic correlation analysis of trading volumes: A study of Chinese stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 838-854.
    4. Niu, Hongli & Wang, Jun, 2017. "Return volatility duration analysis of NYMEX energy futures and spot," Energy, Elsevier, vol. 140(P1), pages 837-849.
    5. Su, Zhi & Shu, Tengjia & Yin, Libo, 2018. "The pricing effect of the common pattern in firm-level idiosyncratic volatility: Evidence from A-Share stocks of China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 218-235.
    6. Niu, Hongli & Wang, Jun & Liu, Cheng, 2018. "Analysis of crude oil markets with improved multiscale weighted permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 389-402.

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