Non-Leading Eigenvalue Distributions, RMT, and Correlations

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

Date Written: July 11, 2016

Abstract

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: https://ssrn.com/abstract=2808055 or http://dx.doi.org/10.2139/ssrn.2808055

Jan Dash (Contact Author)

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

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
41
Abstract Views
508
PlumX Metrics