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Lead-lag cross-sectional structure and detection of correlated–anticorrelated regime shifts: Application to the volatilities of inflation and economic growth rates

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  • Zhou, Wei-Xing
  • Sornette, Didier

Abstract

We have recently introduced the “thermal optimal path” (TOP) method to investigate the real-time lead-lag structure between two time series. The TOP method consists in searching for a robust noise-averaged optimal path of the distance matrix along which the two time series have the greatest similarity. Here, we generalize the TOP method by introducing a more general definition of distance which takes into account possible regime shifts between positive and negative correlations. This generalization to track possible changes of correlation signs is able to identify possible transitions from one convention (or consensus) to another. Numerical simulations on synthetic time series verify that the new TOP method performs as expected even in the presence of substantial noise. We then apply it to investigate changes of convention in the dependence structure between the historical volatilities of the USA inflation rate and economic growth rate. Several measures show that the new TOP method significantly outperforms standard cross-correlation methods.

Suggested Citation

  • Zhou, Wei-Xing & Sornette, Didier, 2007. "Lead-lag cross-sectional structure and detection of correlated–anticorrelated regime shifts: Application to the volatilities of inflation and economic growth rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 287-296.
  • Handle: RePEc:eee:phsmap:v:380:y:2007:i:c:p:287-296
    DOI: 10.1016/j.physa.2007.02.114
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    References listed on IDEAS

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    1. Zhou, Wei-Xing & Sornette, Didier, 2006. "Non-parametric determination of real-time lag structure between two time series: The "optimal thermal causal path" method with applications to economic data," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 195-224, March.
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    Citations

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    Cited by:

    1. Yao, Can-Zhong & Lin, Ji-Nan & Lin, Qing-Wen & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2016. "A study of causality structure and dynamics in industrial electricity consumption based on Granger network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 297-320.
    2. repec:eee:phsmap:v:483:y:2017:i:c:p:299-308 is not listed on IDEAS
    3. repec:eee:phsmap:v:486:y:2017:i:c:p:535-541 is not listed on IDEAS
    4. Guo, Kun & Sun, Yi & Qian, Xin, 2017. "Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 390-396.
    5. Wang, Xuan & Guo, Kun & Lu, Xiaolin, 2016. "The long-run dynamic relationship between exchange rate and its attention index: Based on DCCA and TOP method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 108-115.
    6. repec:eee:intfin:v:49:y:2017:i:c:p:173-183 is not listed on IDEAS
    7. Jia, Rui-Lin & Wang, Dong-Hua & Tu, Jing-Qing & Li, Sai-Ping, 2016. "Correlation between agricultural markets in dynamic perspective—Evidence from China and the US futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 83-92.
    8. Kun Guo & Wei-Xing Zhou & Si-Wei Cheng & Didier Sornette, 2011. "The US stock market leads the Federal funds rate and Treasury bond yields," Papers 1102.2138, arXiv.org.
    9. Gong, Chen-Chen & Ji, Shen-Dan & Su, Li-Ling & Li, Sai-Ping & Ren, Fei, 2016. "The lead–lag relationship between stock index and stock index futures: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 63-72.

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