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How Volatilities Nonlocal in Time Affect the Price Dynamics in Complex Financial Systems

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  • Lei Tan
  • Bo Zheng
  • Jun-Jie Chen
  • Xiong-Fei Jiang

Abstract

What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.

Suggested Citation

  • Lei Tan & Bo Zheng & Jun-Jie Chen & Xiong-Fei Jiang, 2015. "How Volatilities Nonlocal in Time Affect the Price Dynamics in Complex Financial Systems," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-16, February.
    • Handle: RePEc:plo:pone00:0118399
    DOI: 10.1371/journal.pone.0118399
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    Cited by:

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    2. Yan Li & Bo Zheng & Ting-Ting Chen & Xiong-Fei Jiang, 2017. "Fluctuation-driven price dynamics and investment strategies," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-15, December.
    3. Lei Tan & Jun-Jie Chen & Bo Zheng & Fang-Yan Ouyang, 2016. "Exploring Market State and Stock Interactions on the Minute Timescale," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    4. T. T. Chen & B. Zheng & Y. Li & X. F. Jiang, 2017. "New approaches in agent-based modeling of complex financial systems," Papers 1703.06840, arXiv.org.
    5. Chen, Ting-Ting & Zheng, Bo & Li, Yan & Jiang, Xiong-Fei, 2018. "Information driving force and its application in agent-based modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 593-601.

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