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Accurate estimator of correlations between asynchronous signals

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  • Tóth, Bence
  • Kertész, János
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    Abstract

    The estimation of the correlation between time series is often hampered by the asynchronicity of the signals. Cumulating data within a time window suppresses this source of noise but weakens the statistics. We present a method to estimate correlations without applying long time windows. We decompose the correlations of data cumulated over a long window using decay of lagged correlations as calculated from short window data. This increases the accuracy of the estimated correlation significantly and decreases the necessary effort of calculations both in real and computer experiments.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0378437108010881
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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 388 (2009)
    Issue (Month): 8 ()
    Pages: 1696-1705

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    Handle: RePEc:eee:phsmap:v:388:y:2009:i:8:p:1696-1705

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Asynchronous signals; Correlation estimation;

    References

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    1. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 68-104.
    2. Precup, Ovidiu V. & Iori, Giulia, 2004. "A comparison of high-frequency cross-correlation measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 252-256.
    3. Maria Elvira Mancino & Paul Malliavin, 2002. "Fourier series method for measurement of multivariate volatilities," Finance and Stochastics, Springer, vol. 6(1), pages 49-61.
    4. Scalas, Enrico, 2006. "The application of continuous-time random walks in finance and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 225-239.
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    Cited by:
    1. Materassi, Donatello & Innocenti, Giacomo, 2009. "Unveiling the connectivity structure of financial networks via high-frequency analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3866-3878.

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