IDEAS home Printed from https://ideas.repec.org/r/taf/quantf/v5y2005i6p577-591.html
   My bibliography  Save this item

Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Wei-Xing Zhou & Guo-Hua Mu & Si-Wei Chen & Didier Sornette, "undated". "Strategies used as Spectroscopy of Financial Markets Reveal New Stylized Facts," Working Papers ETH-RC-11-005, ETH Zurich, Chair of Systems Design.
  2. 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.
  3. 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.
  4. Yang, Yan-Hong & Shao, Ying-Hui, 2020. "Time-dependent lead-lag relationships between the VIX and VIX futures markets," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
  5. Liu, Wenwen & Gui, Yiming & Qiao, Gaoxiu, 2022. "Dynamics lead-lag relationship of jumps among Chinese stock index and futures market during the Covid-19 epidemic," Research in International Business and Finance, Elsevier, vol. 61(C).
  6. Kun Guo & Wei-Xing Zhou & Si-Wei Cheng & Didier Sornette, 2011. "The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-9, August.
  7. Hao Meng & Hai-Chuan Xu & Wei-Xing Zhou & Didier Sornette, 2017. "Symmetric thermal optimal path and time-dependent lead-lag relationship: novel statistical tests and application to UK and US real-estate and monetary policies," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 959-977, June.
  8. Lai, Lin & Guo, Kun, 2017. "The performance of one belt and one road exchange rate: Based on improved singular spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 299-308.
  9. Zhang, Yongjie & Zhang, Zuochao & Liu, Lanbiao & Shen, Dehua, 2017. "The interaction of financial news between mass media and new media: Evidence from news on Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 535-541.
  10. Xu, Hai-Chuan & Zhou, Wei-Xing & Sornette, Didier, 2017. "Time-dependent lead-lag relationship between the onshore and offshore Renminbi exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 49(C), pages 173-183.
  11. Shao, Ying-Hui & Yang, Yan-Hong & Shao, Hao-Lin & Stanley, H. Eugene, 2019. "Time-varying lead–lag structure between the crude oil spot and futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 723-733.
  12. Shao, Ying-Hui & Yang, Yan-Hong & Zhou, Wei-Xing, 2022. "How does economic policy uncertainty comove with stock markets: New evidence from symmetric thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
  13. Peng Yue & Yaodong Fan & Jonathan A. Batten & Wei-Xing Zhou, 2020. "Information transfer between stock market sectors: A comparison between the USA and China," Papers 2004.07612, arXiv.org.
  14. Yuan, Xianghui & Jin, Liwei & Lian, Feng, 2021. "The lead–lag relationship between Chinese mainland and Hong Kong stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
  15. Yousra Trichilli & Mouna Abdelhédi & Mouna Boujelbène Abbes, 2020. "The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 261-279, May.
  16. 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.
  17. Paul Gaskell & Frank McGroarty & Thanassis Tiropanis, 2014. "Signal Diffusion Mapping: Optimal Forecasting with Time Varying Lags," Papers 1409.6443, arXiv.org.
  18. Stübinger, Johannes, 2018. "Statistical arbitrage with optimal causal paths on high-frequencydata of the S&P 500," FAU Discussion Papers in Economics 01/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  19. Zhicheng Liang & Junwei Wang & Kin Keung Lai, 2020. "Dependence Structure Analysis and VaR Estimation Based on China’s and International Gold Price: A Copula Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 169-193, February.
  20. Gao, Yang & Zhao, Kun & Wang, Chao & Liu, Chao, 2020. "The dynamic relationship between internet attention and stock market liquidity: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
  21. Fei Ren & Mei-Ling Cai & Sai-Ping Li & Xiong Xiong & Zhang-HangJian Chen, 2023. "A Multi-market Comparison of the Intraday Lead–Lag Relations Among Stock Index-Based Spot, Futures and Options," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 1-28, June.
  22. 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.
  23. 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.
  24. Chen, Zhang-HangJian & Ren, Fei & Yang, Ming-Yuan & Lu, Feng-Zhi & Li, Sai-Ping, 2023. "Dynamic lead–lag relationship between Chinese carbon emission trading and stock markets under exogenous shocks," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 295-305.
  25. Liwei Jin & Xianghui Yuan & Jun Long & Xiang Li & Feng Lian, 2022. "Price discovery in the CSI 300 Index derivatives markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1352-1368, July.
  26. Yan-Hong Yang & Ying-Hui Shao, 2019. "Time-dependent lead-lag relationships between the VIX and VIX futures markets," Papers 1910.13729, arXiv.org.
  27. Yao, Can-Zhong & Li, Hong-Yu, 2020. "Time-varying lead–lag structure between investor sentiment and stock market," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
  28. Jiang, Tao & Bao, Si & Li, Long, 2019. "The linear and nonlinear lead–lag relationship among three SSE 50 Index markets: The index futures, 50ETF spot and options markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 878-893.
  29. Stefanos Bennett & Mihai Cucuringu & Gesine Reinert, 2022. "Lead-lag detection and network clustering for multivariate time series with an application to the US equity market," Papers 2201.08283, arXiv.org.
  30. Ren, Fei & Ji, Shen-Dan & Cai, Mei-Ling & Li, Sai-Ping & Jiang, Xiong-Fei, 2019. "Dynamic lead–lag relationship between stock indices and their derivatives: A comparative study between Chinese mainland, Hong Kong and US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 709-723.
  31. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.