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Phase correlation of foreign exchange time series

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  • Wu, Ming-Chya

Abstract

Correlation of foreign exchange rates in currency markets is investigated based on the empirical data of USD/DEM and USD/JPY exchange rates for a period from February 1 1986 to December 31 1996. The return of exchange time series is first decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition method. The instantaneous phases of the resultant IMFs calculated by the Hilbert transform are then used to characterize the behaviors of pricing transmissions, and the correlation is probed by measuring the phase differences between two IMFs in the same order. From the distribution of phase differences, our results show explicitly that the correlations are stronger in daily time scale than in longer time scales. The demonstration for the correlations in periods of 1986–1989 and 1990–1993 indicates two exchange rates in the former period were more correlated than in the latter period. The result is consistent with the observations from the cross-correlation calculation.

Suggested Citation

  • Wu, Ming-Chya, 2007. "Phase correlation of foreign exchange time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 633-642.
    • Handle: RePEc:eee:phsmap:v:375:y:2007:i:2:p:633-642
    DOI: 10.1016/j.physa.2006.09.018
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    Citations

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    Cited by:

    1. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2016. "Anomalous volatility scaling in high frequency financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 434-445.
    2. A. Fronzetti Colladon & F. Grippa & B. Guardabascio & G. Costante & F. Ravazzolo, 2021. "Forecasting consumer confidence through semantic network analysis of online news," Papers 2105.04900, arXiv.org, revised Jul 2023.
    3. Prince Osei Mensah & Anokye M. Adam, 2020. "Copula-Based Assessment of Co-Movement and Tail Dependence Structure Among Major Trading Foreign Currencies in Ghana," Risks, MDPI, vol. 8(2), pages 1-20, June.
    4. Boakye, Robert Owusu & Mensah, Lord Kwaku & Kang, Sang Hoon & Osei, Kofi Acheampong, 2023. "Foreign exchange market return spillovers and connectedness among African countries," International Review of Financial Analysis, Elsevier, vol. 86(C).
    5. Liu, Li-Zhi & Qian, Xi-Yuan & Lu, Heng-Yao, 2010. "Cross-sample entropy of foreign exchange time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4785-4792.
    6. Noemi Nava & T. Di Matteo & Tomaso Aste, 2015. "Anomalous volatility scaling in high frequency financial data," Papers 1503.08465, arXiv.org, revised Dec 2015.
    7. Ouyang, Fang-Yan & Zheng, Bo & Jiang, Xiong-Fei, 2019. "Dynamic fluctuations of cross-correlations in multi-time scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 515-521.
    8. Lin, Aijing & Shang, Pengjian & Zhong, Bo, 2014. "Hidden cross-correlation patterns in stock markets based on permutation cross-sample entropy and PCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 259-272.
    9. Simona Moagăr-Poladian & Dorina Clichici & Cristian-Valeriu Stanciu, 2019. "The Comovement of Exchange Rates and Stock Markets in Central and Eastern Europe," Sustainability, MDPI, vol. 11(14), pages 1-22, July.
    10. Albulescu, Claudiu Tiberiu & Aubin, Christian & Goyeau, Daniel & Tiwari, Aviral Kumar, 2018. "Extreme co-movements and dependencies among major international exchange rates: A copula approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 56-69.
    11. Shi, Wenbin & Shang, Pengjian & Xia, Jianan & Yeh, Chien-Hung, 2016. "The coupling analysis between stock market indices based on permutation measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 222-231.
    12. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    13. Kaijian He & Hongqian Wang & Jiangze Du & Yingchao Zou, 2016. "Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology," Energies, MDPI, vol. 9(11), pages 1-11, November.
    14. Shangkun Deng & Kazuki Yoshiyama & Takashi Mitsubuchi & Akito Sakurai, 2015. "Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 49-89, January.

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