Efficient Rank Reduction of Correlation Matrices

Igor Grubisic and Raoul Pietersz ()
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Igor Grubisic: Utrecht University

Finance from University Library of Munich, Germany

Abstract: Geometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show, in numerical tests, that our methods compare favourably to the existing methods in the literature. The connection with the Lagrange multiplier method is established, along with an identification of whether a local minimum is a global minimum. An additional benefit of the geometric approach is that any weighted norm can be applied. The problem of finding the nearest low-rank correlation matrix occurs as part of the calibration of multi-factor interest rate market models to correlation.

Keywords: geometric optimisation; correlation matrix; rank; LIBOR market model (search for similar items in EconPapers)
JEL-codes: G13 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2005-02-11
New Economics Papers: this item is included in nep-fin
Note: Type of Document - pdf; pages: 21
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Citations: View citations in EconPapers (13)

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Working Paper: Efficient Rank Reduction of Correlation Matrices (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpfi:0502007

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