Efficient Rank Reduction of Correlation Matrices
AbstractGeometric optimisation algorithms are developed that efficiently find the nearest low-rankcorrelation matrix. We show, in numerical tests, that our methods compare favourably to theexisting methods in the literature. The connection with the Lagrange multiplier method isestablished, along with an identification of whether a local minimum is a global minimum. Anadditional benefit of the geometric approach is that any weighted norm can be applied. Theproblem of finding the nearest low-rank correlation matrix occurs as part of the calibration ofmulti-factor interest rate market models to correlation.
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Bibliographic InfoPaper provided by 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. in its series Research Paper with number ERS-2005-009-F&A.
Date of creation: 03 Apr 2005
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correlation matrix; LIBOR market model; Rank; geometric optimisation;
Other versions of this item:
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-09-29 (All new papers)
- NEP-ETS-2005-09-29 (Econometric Time Series)
- NEP-FMK-2005-09-29 (Financial Markets)
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