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Analysis of delay correlation matrices

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  • K. B. K. Mayya
  • R. E. Amritkar

Abstract

We construct and analyze symmetrized delay correlation matrices for empirical data sets for atmopheric and financial data to derive information about correlation between different entities of the time series over time. The information about correlations is obtained by comparing the results for the eigenvalue distribution with the analytical results for the independent, identically distributed random data sets. For the atmospheric case we find long term correlations between different entities of the multivariable time series. For the financial time series we find little correlations between different entities over a time delay beyond about two days. Most of the eigenvalues for the symmetrized delay correlation matrices for the financial data are symmetrically distributed about zero. The delay correlation results for the financial data are similar to the analytical results for the random data sets. However there are considerable deviations for the atmospheric data from the random case.

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  • K. B. K. Mayya & R. E. Amritkar, 2006. "Analysis of delay correlation matrices," Papers cond-mat/0601279, arXiv.org.
    • Handle: RePEc:arx:papers:cond-mat/0601279
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    Cited by:

    1. Zdzisław Burda & Andrzej Jarosz & Maciej Nowak & Jerzy Jurkiewicz & Gabor Papp & Ismail Zahed, 2011. "Applying free random variables to random matrix analysis of financial data. Part I: The Gaussian case," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1103-1124.
    2. Brian Opiyo Yalla & Ferdinand Okoth Othieno, 2023. "Modelling delayed correlation between interest rates and equity market returns," SN Business & Economics, Springer, vol. 3(2), pages 1-24, February.
    3. Sandoval, Leonidas Junior, 2013. "To lag or not to lag? How to compare indices of stock markets that operate at different times," Insper Working Papers wpe_319, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

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