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.

中文翻译：

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预期公用事业投资组合的收缩和阈值方法：预测能力分析

在本文中，我们使用各种协方差矩阵估计方法（包括收缩和基于阈值的方法）估计高维、低样本设置中的预期效用投资组合（EUP）。我们对这些方法进行综合实验，使用全局最小方差 (GMV) 投资组合的平均样本外方差 (AOV) 和 EUP 的平均样本外效用 (AOU)。此外，我们还提出了一种实用方法，供基金经理根据经验数据、依靠 AOV 和 AOU 绩效指标来选择最佳模型。结果表明，在高维设置中，基于收缩的方法优于基于阈值的方法，其中非线性收缩特别有效。