SSA, Random Matrix Theory, and Noise-Reduced Correlations

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

See all articles by Jan Dash

Jan Dash

Fordham University; Bloomberg L.P.

Xipei Yang

Bloomberg L.P.

Mario Bondioli

Bloomberg L.P.

Harvey J. Stein

Two Sigma; Columbia University - Department of Mathematics

Date Written: September 11, 2016

Abstract

This is the third paper in a series devoted to obtaining noise-reduced, stable correlations by smoothing time series using Singular Spectrum Analysis, or SSA. Here we show that the SSA-based correlations are superior in terms of noise reduction, employing a number of simple tests using Random Matrix Theory (RMT) constructs. In each case, the correlations obtained using SSA-smoothed time series are further from noise than are conventional correlations. “Noise” here is defined by a zero-correlation Wishart random matrix WRM composed of correlations between series filled with independent Gaussian random numbers.

Keywords: Singular Spectrum Analysis, SSA, Random Matrix Theory, RMT, Wishart, correlations, stable, noise-reduced

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

Suggested Citation

Dash, Jan and Yang, Xipei and Bondioli, Mario and Stein, Harvey J., SSA, Random Matrix Theory, and Noise-Reduced Correlations (September 11, 2016). Available at SSRN: https://ssrn.com/abstract=2808027 or http://dx.doi.org/10.2139/ssrn.2808027

Jan Dash (Contact Author)

Fordham University ( email )

113 West 60th Street
New York, NY 10023
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Bloomberg L.P. ( email )

731 Lexington Ave
New York, NY 10022
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Xipei Yang

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

Mario Bondioli

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

Harvey J. Stein

Two Sigma ( email )

100 6th Ave
New York, NY 10013
United States
10013 (Fax)

Columbia University - Department of Mathematics ( email )

New York, NY
United States

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