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Guhr, T.

doi:10.5506/aphyspolb.42.1045

Cited by 1 publication

(1 citation statement)

References 11 publications

(19 reference statements)

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“…It can explain the statistical properties of energy levels in complex nuclei well. The RMT method is a useful method for eliminating the randomness in the actual correlation matrix [16][17][18][19][20][21]. Recently, Laloux et al (1999) [22] and Plerou et al (1999) [27] analyzed the correlation matrix of financial time series by the RMT method.…”

Section: Introductionmentioning

confidence: 99%

Statistical properties of cross-correlation in the Korean stock market

Eom

Wang

et al. 2010

Eur. Phys. J. B

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We investigate the statistical properties of the correlation matrix between individual stocks traded in the Korean stock market using the random matrix theory (RMT) and observe how these affect the portfolio weights in the Markowitz portfolio theory. We find that the distribution of the correlation matrix is positively skewed and changes over time. We find that the eigenvalue distribution of original correlation matrix deviates from the eigenvalues predicted by the RMT, and the largest eigenvalue is 52 times larger than the maximum value among the eigenvalues predicted by the RMT. The β 473 coefficient, which reflect the largest eigenvalue property, is 0.8, while one of the eigenvalues in the RMT is approximately zero. Notably, we show that the entropy function E(σ) with the portfolio risk σ for the original and filtered correlation matrices are consistent with a power-law function, E(σ) ∼ σ −γ , with the exponent γ ∼ 2.92 and those for Asian currency crisis decreases significantly.

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