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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
Article
  • 64 Downloads

Abstract

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.

Keywords

CNH CNY EMD lead-lag relationship onshore and offshore markets 

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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

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