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Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701014/ https://www.ncbi.nlm.nih.gov/pubmed/23844016 http://dx.doi.org/10.1371/journal.pone.0067503 |
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author | Bartz, Daniel Hatrick, Kerr Hesse, Christian W. Müller, Klaus-Robert Lemm, Steven |
author_facet | Bartz, Daniel Hatrick, Kerr Hesse, Christian W. Müller, Klaus-Robert Lemm, Steven |
author_sort | Bartz, Daniel |
collection | PubMed |
description | Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. |
format | Online Article Text |
id | pubmed-3701014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37010142013-07-10 Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization Bartz, Daniel Hatrick, Kerr Hesse, Christian W. Müller, Klaus-Robert Lemm, Steven PLoS One Research Article Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. Public Library of Science 2013-07-03 /pmc/articles/PMC3701014/ /pubmed/23844016 http://dx.doi.org/10.1371/journal.pone.0067503 Text en © 2013 Bartz et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Bartz, Daniel Hatrick, Kerr Hesse, Christian W. Müller, Klaus-Robert Lemm, Steven Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization |
title | Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization |
title_full | Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization |
title_fullStr | Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization |
title_full_unstemmed | Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization |
title_short | Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization |
title_sort | directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701014/ https://www.ncbi.nlm.nih.gov/pubmed/23844016 http://dx.doi.org/10.1371/journal.pone.0067503 |
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