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

18 Pages Posted: 11 Jul 2016 Last revised: 22 Jul 2016

See all articles by Jan Dash

Jan Dash

Bloomberg LP

Xipei Yang

Bloomberg L.P.

Harvey J. Stein

Bloomberg L.P.; Columbia University - Department of Mathematics

Mario Bondioli

Bloomberg L.P.

Date Written: July 11, 2016

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

Suggested Citation

Dash, Jan and Yang, Xipei and Stein, Harvey J. and Bondioli, Mario, Stable Reduced-Noise 'Macro' SSA - Based Correlations for Long-Term Counterparty Risk Management (July 11, 2016). Available at SSRN: https://ssrn.com/abstract=2808015 or http://dx.doi.org/10.2139/ssrn.2808015

Jan Dash (Contact Author)

Bloomberg LP ( email )

731 Lexington Ave
New York, NY 10022
United States

Xipei Yang

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

Harvey J. Stein

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States
212 617 3059 (Phone)

Columbia University - Department of Mathematics ( email )

New York, NY
United States

Mario Bondioli

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

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