Publication Date
2025
Document Type
Dissertation/Thesis
First Advisor
Krislock, Nathan
Degree Name
M.S. (Master of Science)
Legacy Department
Department of Mathematical Sciences
Abstract
This thesis investigates efficient algorithms for computing the Nearest Correlation Matrix (NCM) under incomplete financial data. Correlation matrices are vital in portfolio optimization and risk management, yet empirical estimates often violate symmetry, positive semidefiniteness, and unit diagonal conditions due to missing observations. Two projection-based methods are analyzed: the Modified Alternating Projections (MAP) and Anderson Acceleration (AA). Theoretical analysis using convex optimization and normal cone characterization supports numerical evaluation on synthetic and real-world stock-return matrices (550×550, 2020–2025). Missing data are modeled through Missing Completely at Random (MCAR) and Not Missing at Random (NMAR) mechanisms. The results show that AA converges faster and remains robust at higher missingness levels, preserving the validity of the matrix. The findings highlight Anderson Acceleration as an efficient and reliable tool for estimating valid correlation matrices in financial modeling when datasets are incomplete.
Recommended Citation
Oyeyinka, Ibrahim Eniola, "Efficient Algorithms for Nearest Correlation Matrix Computation with Missing Data" (2025). Graduate Research Theses & Dissertations. 8175.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/8175
Extent
76 pages
Language
en
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
