Shrinkage and thresholding approaches for expected utility portfolios: An analysis in terms of predictive ability

Sumanjay Dutta and Shashi Jain

Finance Research Letters, 2024, vol. 64, issue C

Abstract: In this paper, we estimate Expected Utility Portfolios (EUPs) in high-dimensional, low-sample settings using various covariance matrix estimation methods, including shrinkage and thresholding-based methods. We perform synthetic experiments comparing these methods, using Average Out-of-Sample Variance (AOV) for Global Minimum Variance (GMV) portfolios and Average Out-of-Sample Utility (AOU) for EUPs. Additionally, we propose a practical method for fund managers to select optimal models based on empirical data, relying on AOV and AOU performance measures. The results indicate that shrinkage-based methods outperform thresholding-based ones in high-dimensional settings, with non-linear shrinkage being particularly effective.

Keywords: Shrinkage estimators; Covariance matrix estimation; Predictive ability; Mean–variance optimal portfolios (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004562

DOI: 10.1016/j.frl.2024.105426

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