Efficient Rank Reduction of Correlation Matrices
AbstractGeometric 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.
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Bibliographic InfoPaper provided by EconWPA in its series Finance with number 0502007.
Length: 21 pages
Date of creation: 11 Feb 2005
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Note: Type of Document - pdf; pages: 21
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geometric optimisation; correlation matrix; rank; LIBOR market model;
Other versions of this item:
- Grubisic, I. & Pietersz, R., 2005. "Efficient Rank Reduction of Correlation Matrices," ERIM Report Series Research in Management ERS-2005-009-F&A, 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 Uni.
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
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