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

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  • Dutta, Sumanjay
  • Jain, Shashi

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.

Suggested Citation

  • Dutta, Sumanjay & Jain, Shashi, 2024. "Shrinkage and thresholding approaches for expected utility portfolios: An analysis in terms of predictive ability," Finance Research Letters, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004562
    DOI: 10.1016/j.frl.2024.105426
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