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A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market

  • Michael C. M\"unnix
  • Rudi Sch\"afer
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    We analyze the statistical dependency structure of the S&P 500 constituents in the 4-year period from 2007 to 2010 using intraday data from the New York Stock Exchange's TAQ database. With a copula-based approach, we find that the statistical dependencies are very strong in the tails of the marginal distributions. This tail dependence is higher than in a bivariate Gaussian distribution, which is implied in the calculation of many correlation coefficients. We compare the tail dependence to the market's average correlation level as a commonly used quantity and disclose an nearly linear relation.

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    File URL: http://arxiv.org/pdf/1102.1099
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    Paper provided by arXiv.org in its series Papers with number 1102.1099.

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    Date of creation: Feb 2011
    Date of revision: Mar 2011
    Handle: RePEc:arx:papers:1102.1099
    Contact details of provider: Web page: http://arxiv.org/

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    1. Y. Malevergne & D. Sornette, 2001. "Testing the Gaussian Copula Hypothesis for Financial Assets Dependences," Papers cond-mat/0111310, arXiv.org.
    2. Viviana Fernandez, 2006. "Copula-based measures of dependence structure in assets returns," Documentos de Trabajo 228, Centro de Economía Aplicada, Universidad de Chile.
    3. Joshua V. Rosenberg & Til Schuermann, 2004. "A general approach to integrated risk management with skewed, fat-tailed risks," Staff Reports 185, Federal Reserve Bank of New York.
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