Journal of Systems Science and Complexity

, Volume 31, Issue 3, pp 734–749 | Cite as

A Hybrid Approach for Studying the Lead-Lag Relationships Between China’s Onshore and Offshore Exchange Rates Considering the Impact of Extreme Events

  • Yunjie Wei
  • Qi Wei
  • Shouyang Wang
  • Kin Keung Lai


Understanding the characteristics of the dynamic relationship between the onshore Renminbi (CNY) and the offshore Renminbi (CNH) exchange rates considering the impact of some extreme events is very important and it has wide implications in several areas such as hedging. For better estimating the dynamic relationship between CNY and CNH, the Granger-causality test and Bry-Boschan Business Cycle Dating Algorithm were employed in this paper. Due to the intrinsic complexity of the lead-lag relationships between CNY and CNH, the empirical mode decomposition (EMD) algorithm is used to decompose those time series data into several intrinsic mode function (IMF) components and a residual sequence, from high to low frequency. Based on the frequencies, the IMFs and a residual sequence are combined into three components, identified as short-term composition caused by some market activities, medium-term composition caused by some extreme events and the long-term trend. The empirical results indicate that when it only matters the short-term market activities, CNH always leads CNY; while the medium-term impact caused by those extreme events may alternate the lead-lag relationships between CNY and CNH.


CNH CNY EMD lead-lag relationship onshore and offshore markets 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Fratzscher M and Mehl A, China’s dominance hypothesis and the emergence of a tri-polar global currency system, The Economic Journal, 2014, 124(581): 1343–1370.CrossRefGoogle Scholar
  2. [2]
    Henning C R, Choice and coercion in East Asian exchange rate regimes, Peterson Institute for International Economics, Working Paper, 2012.Google Scholar
  3. [3]
    Han A, Lai K K, Wang S, et al., An interval method for studying the relationship between the Australian dollar exchange rate and the gold price, Journal of Systems Science and Complexity, 2012, 25(1): 121–132.MathSciNetCrossRefzbMATHGoogle Scholar
  4. [4]
    Subramanian A and Kessler M, The Renminbi Bloc is Here: Asia down, rest of the World to go?, Journal of Globalization and Development, 2013, 4(1): 49–94.CrossRefGoogle Scholar
  5. [5]
    Xie H and Wang S, A new approach to model financial markets, Journal of Systems Science and Complexity, 2013, 26(3): 432–440.MathSciNetCrossRefzbMATHGoogle Scholar
  6. [6]
    Ghodsi M and Yarmohammadi M, Exchange rate forecasting with optimum singular spectrum analysis, Journal of Systems Science and Complexity, 2014, 27(1): 47–55.CrossRefzbMATHGoogle Scholar
  7. [7]
    Shu C, He D, and Cheng X, One currency, two markets: The renminbi’s growing influence in Asia-Pacific, China Economic Review, 2015, 33: 163–178.CrossRefGoogle Scholar
  8. [8]
    Gagnon J E and Troutman K, Internationalization of the renminbi: The role of trade settlement, Peterson Institute for International Economics, Working Paper, 2014.Google Scholar
  9. [9]
    Granger CWJ, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 1969, 37(3): 424–438.CrossRefzbMATHGoogle Scholar
  10. [10]
    De Jong F and Nijman T, High frequency analysis of lead-lag relationships between financial markets, Journal of Empirical Finance, 1997, 4(2–3): 259–277.CrossRefGoogle Scholar
  11. [11]
    Owyong D, Wong W K, and Horowitz I, Cointegration and causality among the onshore and offshore markets for China’s currency, Journal of Asian Economics, 2015, 41: 20–38.CrossRefGoogle Scholar
  12. [12]
    Gong C C, Ji S D, Su L L, et al., The lead-lag relationship between stock index and stock index futures: A thermal optimal path method, Physica A: Statistical Mechanics and Its Applications, 2016, 444: 63–72.CrossRefGoogle Scholar
  13. [13]
    Ghosh A, Cointegration and error correction models: Intertemporal causality between index and futures prices, Journal of Futures Markets, 1993, 13(2): 193–198.CrossRefGoogle Scholar
  14. [14]
    Shyy G, Vijayraghavan V, and Scott-Quinn B, A further investigation of the lead-lag relationship between the cash market and stock index futures market with the use of bid/ask quotes: The case of France, Journal of Futures Markets, 1996, 16(4): 405–420.CrossRefGoogle Scholar
  15. [15]
    Judge A and Reancharoen T, An empirical examination of the lead-lag relationship between spot and futures markets: Evidence from Thailand, Pacific-Basin Finance Journal, 2014, 29: 335–358.CrossRefGoogle Scholar
  16. [16]
    Cheung Y W and Rime D, The offshore renminbi exchange rate: Microstructure and links to the onshore market, Journal of International Money and Finance, 2014, 49: 170–189.CrossRefGoogle Scholar
  17. [17]
    Bry G and Boschan C, Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, National Bureau of Economic Research, New York, 1971.Google Scholar
  18. [18]
    Funke M, Shu C, Cheng X, et al., Assessing the CNH-CNY pricing differential: Role of fundamentals, contagion and policy, Journal of International Money and Finance, 2015, 59: 245–262.CrossRefGoogle Scholar
  19. [19]
    Huang N E, Shen Z, Long S R, et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, The Royal Society, 1998, 454(1971): 903–995.MathSciNetCrossRefzbMATHGoogle Scholar
  20. [20]
    Huang N E, Wu M L C, Long S R, et al., A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, The Royal Society, 2003, 459(2037): 2317–2345.MathSciNetCrossRefzbMATHGoogle Scholar
  21. [21]
    Huang N E, Wu M L, Qu W, et al., Applications of Hilbert-Huang transform to non-stationary financial time series analysis, Applied Stochastic Models in Business and Industry, 2003, 19(3): 245–268.MathSciNetCrossRefzbMATHGoogle Scholar
  22. [22]
    Zhang X, Yu L, Wang S, et al., Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method, Energy Economics, 2009, 31(5): 768–778.CrossRefGoogle Scholar
  23. [23]
    Wang S Y, Yu L A, and Lai K K, Crude oil price forecasting with TEI@I methodology, Journal of Systems Science and Complexity, 2005, 18(2): 145–166.zbMATHGoogle Scholar
  24. [24]
    Zhang X, Keung Lai K, and Wang S Y, Did speculative activities contribute to high crude oil prices during 1993 to 2008?. Journal of Systems Science and Complexity, 2009, 22(4): 636–646.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Yunjie Wei
    • 1
    • 2
    • 3
  • Qi Wei
    • 4
    • 5
  • Shouyang Wang
    • 1
    • 2
  • Kin Keung Lai
    • 6
    • 7
  1. 1.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.Center for Forecasting ScienceChinese Academy of SciencesBeijingChina
  3. 3.Department of Management SciencesCity University of Hong KongHong KongChina
  4. 4.School of FinanceCentral University of Finance and EconomicsBeijingChina
  5. 5.China Great Wall Asset Management CorporationBeijingChina
  6. 6.International Business SchoolShaanxi Normal UniversityXi’anChina
  7. 7.Department of Industrial and Manufacturing Systems EngineeringHong Kong UniversityHong KongChina

Personalised recommendations