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Asset Allocation Strategies Using Covariance Matrix Estimators

Asset Allocation Strategies Using Covariance Matrix Estimators AbstractThe covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high-dimensional data is involved in the computation. In this study, we focus on the most important estimators that are applied on a group of Markowitz-type strategies and also on a recently introduced method based on hierarchical tree clustering. The performance tests of the portfolio strategies using different covariance matrix estimators rely on the out-of-sample characteristics of synthetic and real stock data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Universitatis Sapientiae, Economics and Business de Gruyter

Asset Allocation Strategies Using Covariance Matrix Estimators

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Publisher
de Gruyter
Copyright
© 2022 László PáL, published by Sciendo
eISSN
2360-0047
DOI
10.2478/auseb-2022-0008
Publisher site
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Abstract

AbstractThe covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high-dimensional data is involved in the computation. In this study, we focus on the most important estimators that are applied on a group of Markowitz-type strategies and also on a recently introduced method based on hierarchical tree clustering. The performance tests of the portfolio strategies using different covariance matrix estimators rely on the out-of-sample characteristics of synthetic and real stock data.

Journal

Acta Universitatis Sapientiae, Economics and Business – de Gruyter

Published: Sep 1, 2022

Keywords: portfolio optimization; covariance matrix estimators; G11; C61

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