SSA, Random Matrix Theory, and Noise-Reduced Correlations
19 Pages Posted: 11 Jul 2016 Last revised: 20 Sep 2016
Date Written: September 11, 2016
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: Suggested Citation