Efficient Rank Reduction of Correlation Matrices

Grubišić, I. and Raoul Pietersz ()

No ERS-2005-009-F&A Revision_Date: 2007-12-10, Research Paper from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

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)
New Economics Papers: this item is included in nep-ets and nep-fmk
Date: 2005-04-03
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Working Paper: Efficient Rank Reduction of Correlation Matrices (2005)
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