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Application of nonlinear time series analysis techniques to high-frequency currency exchange data

Author

Listed:
  • Strozzi, Fernanda
  • Zaldı́var, José-Manuel
  • Zbilut, Joseph P

Abstract

In this work we have applied nonlinear time series analysis to high-frequency currency exchange data. The time series studied are the exchange rates between the US Dollar and 18 other foreign currencies from within and without the Euro zone. Our goal was to determine if their dynamical behaviours were in some way correlated. The nonexistence of stationarity called for the application of recurrence quantification analysis as a tool for this analysis, and is based on the definition of several parameters that allow for the quantification of recurrence plots. The method was checked using the European Monetary System currency exchanges. The results show, as expected, the high correlation between the currencies that are part of the Euro, but also a strong correlation between the Japanese Yen, the Canadian Dollar and the British Pound. Singularities of the series are also demonstrated taking into account historical events, in 1996, in the Euro zone.

Suggested Citation

  • Strozzi, Fernanda & Zaldı́var, José-Manuel & Zbilut, Joseph P, 2002. "Application of nonlinear time series analysis techniques to high-frequency currency exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 520-538.
  • Handle: RePEc:eee:phsmap:v:312:y:2002:i:3:p:520-538
    DOI: 10.1016/S0378-4371(02)00846-4
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    Citations

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    Cited by:

    1. Erzgräber, Hartmut & Strozzi, Fernanda & Zaldívar, José-Manuel & Touchette, Hugo & Gutiérrez, Eugénio & Arrowsmith, David K., 2008. "Time series analysis and long range correlations of Nordic spot electricity market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6567-6574.
    2. Tzagkarakis George & Dionysopoulos Thomas & Achim Alin, 2016. "Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: detecting switching volatility regimes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 75-96, February.
    3. Marisa Faggini & Anna Parziale, 2016. "More than 20 years of chaos in economics," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 15(1), pages 53-69, June.
    4. Belaire-Franch, Jorge, 2004. "Testing for non-linearity in an artificial financial market: a recurrence quantification approach," Journal of Economic Behavior & Organization, Elsevier, vol. 54(4), pages 483-494, August.
    5. Kyrtsou, Catherine & Malliaris, Anastasios G. & Serletis, Apostolos, 2009. "Energy sector pricing: On the role of neglected nonlinearity," Energy Economics, Elsevier, vol. 31(3), pages 492-502, May.
    6. Bastos, João A. & Caiado, Jorge, 2011. "Recurrence quantification analysis of global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1315-1325.
    7. Aparicio, Teresa & Pozo, Eduardo F. & Saura, Dulce, 2008. "Detecting determinism using recurrence quantification analysis: Three test procedures," Journal of Economic Behavior & Organization, Elsevier, vol. 65(3-4), pages 768-787, March.
    8. Sato, Aki-Hiro, 2007. "Frequency analysis of tick quotes on the foreign exchange market and agent-based modeling: A spectral distance approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 258-270.
    9. Strozzi, Fernanda & Zaldívar, José-Manuel & Zbilut, Joseph P., 2007. "Recurrence quantification analysis and state space divergence reconstruction for financial time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 487-499.
    10. Zhou, Wei-Xing & Sornette, Didier, 2006. "Non-parametric determination of real-time lag structure between two time series: The "optimal thermal causal path" method with applications to economic data," Journal of Macroeconomics, Elsevier, vol. 28(1), pages 195-224, March.
    11. Didier Sornette & Wei-Xing Zhou, 2005. "Non-parametric determination of real-time lag structure between two time series: the 'optimal thermal causal path' method," Quantitative Finance, Taylor & Francis Journals, vol. 5(6), pages 577-591.
    12. Catherine Kyrtsou & Michel Terraza, 2010. "Seasonal Mackey–Glass–GARCH process and short-term dynamics," Empirical Economics, Springer, vol. 38(2), pages 325-345, April.
    13. Felicia Ramona Birau, 2012. "Econometric Approach Of Heteroskedasticity On Financial Time Series In A General Framework," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 74-77, December.
    14. Strozzi, F. & Zaldívar, J.M., 2005. "Non-linear forecasting in high-frequency financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 463-479.
    15. D. Sornette & W. -X. Zhou, 2004. "Non-parametric Determination of Real-Time Lag Structure between Two Time Series: the "Optimal Thermal Causal Path" Method," Papers cond-mat/0408166, arXiv.org.
    16. Karagianni Stella & Kyrtsou Catherine, 2011. "Analysing the Dynamics between U.S. Inflation and Dow Jones Index Using Non-Linear Methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(2), pages 1-25, March.
    17. repec:eee:ecomod:v:210:y:2008:i:1:p:58-70 is not listed on IDEAS
    18. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    19. Goswami, B. & Ambika, G. & Marwan, N. & Kurths, J., 2012. "On interrelations of recurrences and connectivity trends between stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4364-4376.

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