An EZI Method to Reduce the Rank of a Correlation Matrix in Financial Modelling
AbstractReducing the number of factors in a model by reducing the rank of a correlation matrix is a problem that often arises in finance, for instance in pricing interest rate derivatives with Libor market models. A simple iterative algorithm for correlation rank reduction is introduced, the eigenvalue zeroing by iteration, EZI, algorithm. Its convergence is investigated and extension presented with particular optimality properties. The performance of EZI is compared with those of other common methods. Different data sets are considered including empirical data from the interest rate market, different possible market cases and criteria, and a calibration case. The EZI algorithm is extremely fast even in computationally complex situations, and achieves a very high level of precision. From these results, the EZI algorithm for financial application has superior performance to the main methods in current use.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Mathematical Finance.
Volume (Year): 13 (2006)
Issue (Month): 4 ()
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- Igor Grubisic & Raoul Pietersz, 2005.
"Efficient Rank Reduction of Correlation Matrices,"
- 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.
- 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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