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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)734-749
Journal / PublicationJournal of Systems Science and Complexity
Volume31
Issue number3
Online published29 Nov 2017
Publication statusPublished - Jun 2018

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.

Research Area(s)

  • CNH, CNY, EMD, lead-lag relationship, onshore and offshore markets

Citation Format(s)

A Hybrid Approach for Studying the Lead-Lag Relationships Between China’s Onshore and Offshore Exchange Rates Considering the Impact of Extreme Events. / Wei, Yunjie; Wei, Qi; Wang, Shouyang et al.
In: Journal of Systems Science and Complexity, Vol. 31, No. 3, 06.2018, p. 734-749.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review