Introduction to Noise-Reduced Correlations Using Singular Spectrum Analysis
12 Pages Posted: 30 Aug 2017
Date Written: August 28, 2017
We summarize new results for estimating correlations for use in risk management. These estimates have better behavior than traditional estimation approaches from both a business standpoint and a technical standpoint. We smooth time series using Singular Spectrum Analysis (SSA) and compute correlations based on these smoothed series. We demonstrate that SSA-based correlation estimates have less noise than standard correlation estimates between unsmoothed series using: the signal-to-noise ratio, and distances from noise using polynomials generalizing the z-score and random matrix theory constructs. New useful analytic estimates for all eigenvalues of a random matrix are described. SSA-based correlations also enjoy superior time stability. Technical aspects are given in four accompanying papers, including extensive analyses of time stability and the noise-reduction tests described in this short paper.
Keywords: Singular Spectrum Analysis, Risk Management, Correlations, Stable, Noise-Cleaned, Polynomials Generalizing Z-Score, Signal-To-Noise Ratio, Random Matrix Theory, Analytic Eigenvalues of Random Matrix, Business Decisions
JEL Classification: C1, C14, C22, C63, E44, F65, G1, Y1
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