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Efficient Rank Reduction of Correlation Matrices

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Author Info

  • Igor Grubisic

    (Utrecht University)

  • Raoul Pietersz

    (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.

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File URL: http://128.118.178.162/eps/fin/papers/0502/0502007.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Finance with number 0502007.

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Length: 21 pages
Date of creation: 11 Feb 2005
Date of revision:
Handle: RePEc:wpa:wuwpfi:0502007

Note: Type of Document - pdf; pages: 21
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Web page: http://128.118.178.162

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Keywords: geometric optimisation; correlation matrix; rank; LIBOR market model;

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References

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  1. Jong, F.C.J.M. de & Driessen, J. & Pelsser, A., 2004. "On the information in the interest rate term structure and option prices," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3159390, Tilburg University.
  2. Qiang Dai & Kenneth Singleton, 2003. "Term Structure Dynamics in Theory and Reality," Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 631-678, July.
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Citations

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Cited by:
  1. Raoul Pietersz & Patrick J. F. Groenen, 2005. "Rank Reduction of Correlation Matrices by Majorization," Finance 0502006, EconWPA.
  2. Raoul Pietersz & Marcel Regenmortel, 2006. "Generic market models," Finance and Stochastics, Springer, vol. 10(4), pages 507-528, December.
  3. Raoul Pietersz & Antoon Pelsser, 2010. "A comparison of single factor Markov-functional and multi factor market models," Review of Derivatives Research, Springer, vol. 13(3), pages 245-272, October.
  4. Mishra, SK, 2007. "Completing correlation matrices of arbitrary order by differential evolution method of global optimization: A Fortran program," MPRA Paper 2000, University Library of Munich, Germany.
  5. Sudhanshu K Mishra, 2013. "Global Optimization of Some Difficult Benchmark Functions by Host-Parasite Coevolutionary Algorithm," Economics Bulletin, AccessEcon, vol. 33(1), pages 1-18.
  6. Massimo Morini & Nick Webber, 2006. "An EZI Method to Reduce the Rank of a Correlation Matrix in Financial Modelling," Applied Mathematical Finance, Taylor & Francis Journals, vol. 13(4), pages 309-331.
  7. Ken-ichi Mitsui & Yoshio Tabata, 2006. "Random Correlation Matrix and De-Noising," Discussion Papers in Economics and Business 06-26, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).

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