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How Independent Are Stocks in an Independent Set of a Market Graph

  • Petr A. KoldanovEmail author
  • Ivan Grechikhin
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 104)

Abstract

The problem of testing hypothesis of independence of random variables describing stock returns for a given set of stocks is considered. Two tests of independence are compared. The first test is the classical maximum likelihood test based on the determinant of a sample covariance matrix. The second test is the pairwise test used for market graph construction. This test is based on testing of pairwise independence of random variables describing stock returns by Pearson correlation test. The main result is the following: the maximum likelihood test is more powerful for a wide class of alternatives. Some examples are given.

Keywords

Stock Market Correlation Matrice Multivariate Normal Distribution Sample Covariance Matrix Pairwise Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors are partly supported by National Research University Higher School of Economics, Russian Federation Government Grant N. 11.G34.31.0057 and RFFI Grant 14-01-00807.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsNizhny NovgorodRussia

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