Non-Leading Eigenvalue Distributions, RMT, and Correlations

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

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Year of publication:	2016
Authors:	Dash, Jan
Other Persons:	Yang, Xipei (contributor)
Publisher:	[2016]: [S.l.] : SSRN
Subject:	Theorie \| Theory \| Korrelation \| Correlation

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