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Directional Variance Adjustment: improving covariance estimates for high-dimensional portfolio optimization

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  • Daniel Bartz
  • Kerr Hatrick
  • Christian W. Hesse
  • Klaus-Robert M\"uller
  • Steven Lemm
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    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.

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    File URL: http://arxiv.org/pdf/1109.3069
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1109.3069.

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    Date of creation: Sep 2011
    Date of revision: Mar 2012
    Handle: RePEc:arx:papers:1109.3069

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    Web page: http://arxiv.org/

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