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Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization

Authors
Bartz, DanielHatrick, KerrHesse, Christian W.Mueller, Klaus-RobertLemm, Steven
Issue Date
3-7월-2013
Publisher
PUBLIC LIBRARY SCIENCE
Citation
PLOS ONE, v.8, no.7
Indexed
SCIE
SCOPUS
Journal Title
PLOS ONE
Volume
8
Number
7
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/102723
DOI
10.1371/journal.pone.0067503
ISSN
1932-6203
Abstract
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.
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