A study of the interplay between the structure variation and fluctuations of the Shanghai stock market
AbstractThe intricate interplay between the variation of the stock network structure and fluctuations of that stock market is increasingly becoming a hot topic. In this work, employing a moving window to scan through every stock price time series over a period from 2 January 2001 to 7 December 2010, we use mutual information to measure the statistical interdependence between stock prices, and we construct a corresponding network for 501 Shanghai stocks in every given window. Then we address the time-varying relationships between the structure variation and fluctuations for the Shanghai stock market. All the results obtained here indicate that at turning points the growing independence of stocks causes the scalefreeness of the degree distribution to be disrupted, and that the Shanghai stock index has little volatility clustering. In contrast, under normality of the market, the stock networks have characteristics of scalefree degree distribution. Furthermore, the degree of volatility clustering is a little higher.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 391 (2012)
Issue (Month): 11 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Mutual information; Stock network; Scalefree degree distribution; Volatility clustering;
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- Tian Qiu & Bo Zheng & Guang Chen, 2010. "Adaptive financial networks with static and dynamic thresholds," Papers 1002.3432, arXiv.org.
- G. Bonanno & F. Lillo & R. N. Mantegna, 2001.
"High-frequency cross-correlation in a set of stocks,"
Taylor & Francis Journals, vol. 1(1), pages 96-104.
- Giovanni Bonanno & Fabrizio Lillo & Rosario N. Mantegna, 2000. "High-frequency Cross-correlation in a Set of Stocks," Papers cond-mat/0009350, arXiv.org, revised Nov 2000.
- Zhang, Yiting & Lee, Gladys Hui Ting & Wong, Jian Cheng & Kok, Jun Liang & Prusty, Manamohan & Cheong, Siew Ann, 2011. "Will the US economy recover in 2010? A minimal spanning tree study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2020-2050.
- Dong-Ming Song & Michele Tumminello & Wei-Xing Zhou & Rosario N. Mantegna, 2011. "Evolution of worldwide stock markets, correlation structure and correlation based graphs," Papers 1103.5555, arXiv.org.
- Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
- Tseng, Jie-Jun & Li, Sai-Ping, 2011. "Asset returns and volatility clustering in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1300-1314.
- Jing Liu & Chi Tse & Keqing He, 2011. "Fierce stock market fluctuation disrupts scalefree distribution," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 817-823.
- Jie-Jun Tseng & Sai-Ping Li, 2010. "Asset returns and volatility clustering in financial time series," Papers 1002.0284, arXiv.org, revised Apr 2011.
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