Precision Matrix Estimation for the Global Minimum Variance Portfolio

Marco Neffelli (), Maria Elena Giuli () and Marina Resta ()
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Marco Neffelli: Alpha Real Capital LLP
Maria Elena Giuli: University of Pavia
Marina Resta: University of Genova

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2021, pp 361-367 from Springer

Abstract: Abstract We use the Minimum Regularised Covariance Determinant Estimator (MRCD) to limit weights’ misspecification within the Global Minimum Variance Portfolio (GMVP) framework. Estimating the precision matrix is a key step that often generates misspecification which translates to resulting portfolio weights, directly affecting the GMVP out-of-sample performance. This effect is exacerbated when data are high-dimensional and non-Normal. To this extent, we propose using the MRCD because is well-designed to deal with high-dimensionality and non-Normality. We perform an extensive Monte Carlo simulation analysis to check the effectiveness of the proposed approach in comparison to the sample estimator. Our analysis demonstrates that the out-of-sample performance of the GMVP benefits from the use of the MRCD estimator: results suggest a reduction in the portfolio turnover at no cost for the portfolio variance and an increase in the portfolio Sharpe ratio.

Keywords: Precision matrix; Global Minimum Variance Portfolio; Estimation error (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78965-7_53

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DOI: 10.1007/978-3-030-78965-7_53

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