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

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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.

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References

  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.

    Article  Google Scholar 

  2. Henning C R, Choice and coercion in East Asian exchange rate regimes, Peterson Institute for International Economics, Working Paper, 2012.

    Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  5. Xie H and Wang S, A new approach to model financial markets, Journal of Systems Science and Complexity, 2013, 26(3): 432–440.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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. Granger CWJ, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 1969, 37(3): 424–438.

    Article  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  13. Ghosh A, Cointegration and error correction models: Intertemporal causality between index and futures prices, Journal of Futures Markets, 1993, 13(2): 193–198.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    MATH  Google Scholar 

  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.

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Shouyang Wang.

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The research was partially supported by the National Natural Science Foundation of China under Grant Nos. 71390330, 71390331, 71390335. The first and third authors are very grateful to the National Nature Science Foundation of China for financial support to this study, the second author is supported by the Postdoctorate Programme of Centre University of Economics and Finance and the Postodctorate Programme of China Great Wall Asset Management Corporation.

This paper was recommended for publication by Editor YANG Cuihong.

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Wei, Y., Wei, Q., Wang, S. et al. A Hybrid Approach for Studying the Lead-Lag Relationships Between China’s Onshore and Offshore Exchange Rates Considering the Impact of Extreme Events. J Syst Sci Complex 31, 734–749 (2018). https://doi.org/10.1007/s11424-017-6281-7

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  • DOI: https://doi.org/10.1007/s11424-017-6281-7

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