Large portfolio allocation based on high-dimensional regression and Kendall’s Tau

Zhang, Z; Mu, Yun; Huang, Lei; Wang, Qin; Yang, Baozhong

doi:10.1080/03610918.2023.2245176

Cited by 1 publication

(1 citation statement)

References 38 publications

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“…Rooted in modern portfolio theory (MPT), portfolio allocation advocates strategically distributing investments across diverse asset classes to optimize the trade-off between risk and return. The MPT theory highlights the significance of not only choosing individual stocks but also the proportional weighting of these stocks in a portfolio [28,29]. Therefore, we investigate the optimal allocation for minimum risk portfolios of stocks, utilizing the estimated covariance matrix of various methods.…”

Section: Real Data Analysis and Portfolio Allocationmentioning

confidence: 99%

Element Aggregation for Estimation of High-Dimensional Covariance Matrices

Yang

2024

Mathematics

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This study addresses the challenge of estimating high-dimensional covariance matrices in financial markets, where traditional sparsity assumptions often fail due to the interdependence of stock returns across sectors. We present an innovative element-aggregation method that aggregates matrix entries to estimate covariance matrices. This method is designed to be applicable to both sparse and non-sparse matrices, transcending the limitations of sparsity-based approaches. The computational simplicity of the method’s implementation ensures low complexity, making it a practical tool for real-world applications. Theoretical analysis then confirms the method’s consistency and effectiveness with its convergence rate in specific scenarios. Additionally, numerical experiments validate the method’s superior algorithmic performance compared to conventional methods, as well as the reduction in relative estimation errors. Furthermore, empirical studies in financial portfolio optimization demonstrate the method’s significant risk management benefits, particularly its ability to effectively mitigate portfolio risk even with limited sample sizes.

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Section: Real Data Analysis and Portfolio Allocationmentioning

confidence: 99%

Element Aggregation for Estimation of High-Dimensional Covariance Matrices

Yang

2024

Mathematics

View full text Add to dashboard Cite

show abstract

Large portfolio allocation based on high-dimensional regression and Kendall’s Tau

Cited by 1 publication

References 38 publications

Element Aggregation for Estimation of High-Dimensional Covariance Matrices

Element Aggregation for Estimation of High-Dimensional Covariance Matrices

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