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Recurrence quantification analysis and state space divergence reconstruction for financial time series analysis

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  • Strozzi, Fernanda
  • Zaldívar, José-Manuel
  • Zbilut, Joseph P.

Abstract

The application of recurrence quantification analysis (RQA) and state space divergence reconstruction for the analysis of financial time series in terms of cross-correlation and forecasting is illustrated using high-frequency time series and random heavy-tailed data sets. The results indicate that these techniques, able to deal with non-stationarity in the time series, may contribute to the understanding of the complex dynamics hidden in financial markets. The results demonstrate that financial time series are highly correlated. Finally, an on-line trading strategy is illustrated and the results shown using high-frequency foreign exchange time series.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:376:y:2007:i:c:p:487-499
    DOI: 10.1016/j.physa.2006.10.020
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    References listed on IDEAS

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    1. Okunev, John & White, Derek, 2003. "Do Momentum-Based Strategies Still Work in Foreign Currency Markets?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(2), pages 425-447, June.
    2. 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.
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    4. 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.
    5. Lisi, Francesco & Medio, Alfredo, 1997. "Is a random walk the best exchange rate predictor?," International Journal of Forecasting, Elsevier, vol. 13(2), pages 255-267, June.
    6. Cao, Liangyue & Soofi, Abdol S., 1999. "Nonlinear deterministic forecasting of daily dollar exchange rates," International Journal of Forecasting, Elsevier, vol. 15(4), pages 421-430, October.
    7. Johnson, Neil F. & Jefferies, Paul & Hui, Pak Ming, 2003. "Financial Market Complexity," OUP Catalogue, Oxford University Press, number 9780198526650.
    8. Ohira, Toru & Sazuka, Naoya & Marumo, Kouhei & Shimizu, Tokiko & Takayasu, Misako & Takayasu, Hideki, 2002. "Predictability of currency market exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 308(1), pages 368-374.
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    Cited by:

    1. Facchini, Angelo & Rubino, Alessandro & Caldarelli, Guido & Di Liddo, Giuseppe, 2019. "Changes to Gate Closure and its impact on wholesale electricity prices: The case of the UK," Energy Policy, Elsevier, vol. 125(C), pages 110-121.
    2. Oleksandr Piskun & Sergii Piskun, 2011. "Recurrence Quantification Analysis of Financial Market Crashes and Crises," Papers 1107.5420, arXiv.org.
    3. 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.
    4. Teresa Aparicio & Dulce Saura, 2013. "Do Exchange Rate Series Present General Dependence? Some Results using Recurrence Quantification Analysis," Journal of Economics and Behavioral Studies, AMH International, vol. 5(10), pages 678-686.
    5. Chen, Wei-Shing, 2011. "Use of recurrence plot and recurrence quantification analysis in Taiwan unemployment rate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1332-1342.
    6. Giuseppe Orlando & Giovanna Zimatore, 2021. "Recurrence Quantification Analysis of Business Cycles," Dynamic Modeling and Econometrics in Economics and Finance, in: Giuseppe Orlando & Alexander N. Pisarchik & Ruedi Stoop (ed.), Nonlinearities in Economics, chapter 0, pages 269-282, Springer.

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