Non-Leading Eigenvalue Distributions, RMT, and Correlations

16 Pages Posted: 11 Jul 2016 Last revised: 20 Sep 2016

See all articles by Jan Dash

Jan Dash

Fordham University; Bloomberg LP

Xipei Yang

Bloomberg L.P.

Date Written: July 11, 2016


We showed that Singular Spectrum Analysis (SSA) applied to time series yields better correlations for risk simulations. This involved comparing SSA-based correlations with standard correlations and to noise, a zero correlation Wishart random matrix (WRM). We complete this testing here. We also present tractable analytic approximate WRM results that we used in the analysis: (1) leading and non-leading eigenvalue distributions of a WRM, (2) eigenvalue spacing of WRMs, and (3) eigenvector components of WRMs.

Keywords: Singular Spectrum Analysis, SSA, Non-leading eigenvalue distribution, Wishart random matrix, RMT, correlations, stable, noise-reduced, eigenvalue spacing, eigenvector components

JEL Classification: C1, C14, C22, C63, E44, F65, G1, Y1

Suggested Citation

Dash, Jan and Yang, Xipei, Non-Leading Eigenvalue Distributions, RMT, and Correlations (July 11, 2016). Available at SSRN: or

Jan Dash (Contact Author)

Fordham University ( email )

113 W. 60th St
New York, NY 10023
United States

Bloomberg LP ( email )

731 Lexington Ave
New York, NY 10022
United States

Xipei Yang

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

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