Stable Reduced-Noise 'Macro' SSA - Based Correlations for Long-Term Counterparty Risk Management

Abstract

We introduce a methodology from geophysics, Singular Spectrum Analysis (SSA), to obtain stable, noise-cleaned correlations for long term risk (e.g. counterparty risk). SSA is applied to time series to smooth them in a robust manner. The SSA-smoothed time series are then used to obtain the correlations. We call these “macro” correlations because they are determined with macroscopic time scales. Stable correlations are desirable to suppress noise from short time scales that make risk measures unstable. If correlations move around, risk measures also move around, making business decisions difficult. SSA-based correlations ameliorate this business problem.

Keywords: Singular Spectrum Analysis, counterparty risk, correlations, stable, noise-cleaned, macro, business decisions

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