Efficient Rank Reduction of Correlation Matrices
(Efficient Rank Reduction of Correlation Matrices)
2005-04-03
Research Paper
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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
Classifications using
Journal of Economic Literature (JEL) Classification System
- C61 : Optimization Techniques; Programming Models; Dynamic Analysis
- G3 : Corporate Finance and Governance
- E43 : Determination of Interest Rates; Term Structure of Interest Rates
- M : Business Administration and Business Economics; Marketing; Accounting
- G13 : Contingent Pricing; Futures Pricing
Automatically Extracted Terms
- y y t
- algorithm
- correlation
- matrix
- manifold
- method
- point
- section
- matrice
- choln
- problem
- space
- optimisation
- rank reduction
- theorem
- model
- convergence
- performance
- function
- vector
