Estimating a Covariance Matrix for Market Risk Management and the Case of Credit Default Swaps

29 Pages Posted: 21 May 2016 Last revised: 13 Mar 2018

See all articles by Richard Neuberg

Richard Neuberg

Columbia University - Department of Statistics

Paul Glasserman

Columbia Business School

Date Written: March 9, 2018

Abstract

We analyze covariance matrix estimation from the perspective of market risk management, where the goal is to obtain accurate estimates of portfolio risk across essentially all portfolios—even those with small standard deviations. We propose a simple but effective visualization tool to assess bias across a wide range of portfolios. We employ a portfolio perspective to determine covariance matrix loss functions particularly suitable for market risk management. Proper regularization of the covariance matrix estimate significantly improves performance. These methods are applied to credit default swaps, for which covariance matrices are used to set portfolio margin requirements for central clearing. Among the methods we test, the graphical lasso estimator performs particularly well. The graphical lasso and a hierarchical clustering estimator also yield economically meaningful representations of market structure through a graphical model and a hierarchy, respectively.

Keywords: Portfolio risk, correlation matrices, matrix loss functions, margin requirements

JEL Classification: C58, G20

Suggested Citation

Neuberg, Richard and Glasserman, Paul, Estimating a Covariance Matrix for Market Risk Management and the Case of Credit Default Swaps (March 9, 2018). Columbia Business School Research Paper No. 16-39, Available at SSRN: https://ssrn.com/abstract=2782107 or http://dx.doi.org/10.2139/ssrn.2782107

Richard Neuberg (Contact Author)

Columbia University - Department of Statistics ( email )

Mail Code 4403
New York, NY 10027
United States

Paul Glasserman

Columbia Business School ( email )

3022 Broadway
403 Uris Hall
New York, NY 10027
United States
212-854-4102 (Phone)
212-316-9180 (Fax)

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