Cleaning Financial Data Using SSA and MSSA

30 Pages Posted: 12 Jul 2016 Last revised: 17 Sep 2016

See all articles by Jan Dash

Jan Dash

Fordham University; Bloomberg LP

Yan Zhang

Bloomberg LP

Date Written: September 11, 2016

Abstract

We introduce a powerful method for cleaning time series - Multi-Channel Singular Spectrum Analysis (MSSA). “Cleaning” means filling data gaps and removing unphysical spikes, which are chronic problems. MSSA utilizes all available information in “time” and “space” with autocorrelations, correlations, and lagged correlations. MSSA performs demonstrably better than other methods. Here we present tests using MSSA to fill data gaps, with positive results. Spike removal is in a separate paper.

Keywords: cleaning time series, Multi-Channel Singular Spectrum Analysis, MSSA, filling data gaps, removing unphysical spikes, correlations, lagged correlations, autocorrelations

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

Suggested Citation

Dash, Jan and Zhang, Yan, Cleaning Financial Data Using SSA and MSSA (September 11, 2016). Available at SSRN: https://ssrn.com/abstract=2808156 or http://dx.doi.org/10.2139/ssrn.2808156

Jan Dash (Contact Author)

Fordham University ( email )

113 W. 60th St
New York, NY 10023
United States

Bloomberg LP ( email )

731 Lexington Ave
New York, NY 10022
United States

Yan Zhang

Bloomberg LP ( email )

731 Lexington Ave
New York, NY 10022
United States

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