Directional Variance Adjustment: improving covariance estimates for high-dimensional portfolio optimization
Portfolio Management
2015-03-19 v3 Computational Engineering, Finance, and Science
Statistical Finance
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 market we show that our proposed method leads to improved portfolio allocation.
Keywords
Cite
@article{arxiv.1109.3069,
title = {Directional Variance Adjustment: improving covariance estimates for high-dimensional portfolio optimization},
author = {Daniel Bartz and Kerr Hatrick and Christian W. Hesse and Klaus-Robert Müller and Steven Lemm},
journal= {arXiv preprint arXiv:1109.3069},
year = {2015}
}