An analysis of Cross-correlations in South African Market data
We apply random matrix theory to compare correlation matrix estimators C obtained from emerging market data. The correlation matrices are constructed from 10 years of daily data for stocks listed on the Johannesburg Stock Exchange (JSE) from January 1993 to December 2002. We test the spectral properties of C against random matrix predictions and find some agreement between the distributions of eigenvalues, nearest neighbour spacings, distributions of eigenvector components and the inverse participation ratios for eigenvectors. We show that interpolating both missing data and illiquid trading days with a zero-order hold increases agreement with RMT predictions. For the more realistic estimation of correlations in an emerging market, we suggest a pairwise measured-data correlation matrix. For the data set used, this approach suggests greater temporal stability for the leading eigenvectors. An interpretation of eigenvectors in terms of trading strategies is given in lieu of classification by economic sectors.
|Date of creation:||Feb 2004|
|Date of revision:||Sep 2006|
|Publication status:||Published in Physica A 375 (2007) 584-598|
|Contact details of provider:|| Web page: http://arxiv.org/|
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- Szilard Pafka & Imre Kondor, 2003. "Estimated Correlation Matrices and Portfolio Optimization," Papers cond-mat/0305475, arXiv.org.
- Drożdż, S. & Kwapień, J. & Grümmer, F. & Ruf, F. & Speth, J., 2001. "Quantifying the dynamics of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 144-153. Full references (including those not matched with items on IDEAS)
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