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Bibliographic Details
Main Authors: Gunther, Nick L., Kercheval, Alec N., Sowunmi, Ololade
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.07692
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author Gunther, Nick L.
Kercheval, Alec N.
Sowunmi, Ololade
author_facet Gunther, Nick L.
Kercheval, Alec N.
Sowunmi, Ololade
contents For a covariance matrix coming from a factor model of returns, we investigate the relationship between the long-only global minimum variance portfolio and the asset exposures to the factors. In the case of a 1-factor model, we provide a rigorous and explicit description of the long-only solution in terms of the parameters of the covariance matrix. For $q>1$ factors, we provide a description of the long-only portfolio in geometric terms. The results are illustrated with empirical daily returns of US stocks.
format Preprint
id arxiv_https___arxiv_org_abs_2603_07692
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Understanding the Long-Only Minimum Variance Portfolio
Gunther, Nick L.
Kercheval, Alec N.
Sowunmi, Ololade
Mathematical Finance
Portfolio Management
Risk Management
62P05 (Primary), 90C20
For a covariance matrix coming from a factor model of returns, we investigate the relationship between the long-only global minimum variance portfolio and the asset exposures to the factors. In the case of a 1-factor model, we provide a rigorous and explicit description of the long-only solution in terms of the parameters of the covariance matrix. For $q>1$ factors, we provide a description of the long-only portfolio in geometric terms. The results are illustrated with empirical daily returns of US stocks.
title Understanding the Long-Only Minimum Variance Portfolio
topic Mathematical Finance
Portfolio Management
Risk Management
62P05 (Primary), 90C20
url https://arxiv.org/abs/2603.07692