Computing Nearest Correlation Matrix via Low-Rank ODE’s Based Technique
Name
symmetry-12-01824.pdf
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761 KB
Format
Adobe PDF
Checksum (MD5)
0651323fe6e6c64e7457b7223c554a63
Author(s)
Rehman, Mutti-Ur
Alzabut, Jehad
Abodayeh, Kamaleldin
Date Issued
November 4, 2020
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Symmetry 12 (11): 1824 (2020)
Version
Final published version
Abstract
For n-dimensional real-valued matrix A, the computation of nearest correlation matrix; that is, a symmetric, positive semi-definite, unit diagonal and off-diagonal entries between −1 and 1 is a problem that arises in the finance industry where the correlations exist between the stocks. The proposed methodology presented in this article computes the admissible perturbation matrix and a perturbation level to shift the negative spectrum of perturbed matrix to become non-negative or strictly positive. The solution to optimization problems constructs a gradient system of ordinary differential equations that turn over the desired perturbation matrix. Numerical testing provides enough evidence for the shifting of the negative spectrum and the computation of nearest correlation matrix.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/sym12111824
