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...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2013-07, Vol.8 (7), p.e67503-e67503
Main Authors: Bartz, Daniel, Hatrick, Kerr, Hesse, Christian W, Müller, Klaus-Robert, Lemm, Steven
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bias
Computer Science
Computer Simulation
Covariance
Covariance matrix
Economic models
Efficiency
Empirical analysis
Engineering
Error analysis
Factor analysis
Factor Analysis, Statistical
Investment analysis
Likelihood Functions
Mathematical models
Mathematics
Models, Statistical
Monte Carlo Method
Multivariate analysis
Optimization
Portfolio management
Principal components analysis
Sample size
Social and Behavioral Sciences
Statistical analysis
Stock exchanges
Stock markets
Studies
Theory
Time series
Variance
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0067503