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图网络风险感知与稀疏低秩的组合管理策略

Graph Network Risk Perception and Sparse Low-rank Portfolio Management Strategy

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【作者】 李爱忠任若恩董纪昌

【Author】 Li Aizhong;Ren Ruoen;Dong Jichang;School of Public Finance & Economics, Shanxi University of Finance and Economics;School of Economics and Management, Beihang University;School of Economics and Management, University of Chinese Academy of Sciences;

【通讯作者】 李爱忠;

【机构】 山西财经大学财政与公共经济学院北京航空航天大学经济管理学院中国科学院大学经济与管理学院

【摘要】 资产的联动性具有很强的网络特性,其风险的传染、蔓延由简单的单向驱动关系逐步演化为网络式的循环互动关系。将风险的传染和溢出纳入投资组合优化配置的框架,深入研究资产的波动集聚效应、风险的网络传播效应以及非线性叠加效应,可为规避投资风险和全面风险管理提供新的视角和思路。本文通过高维稀疏低秩算法和基于图网络结构的熵不确定性网络风险模型,深入挖掘资产特征和捕捉其间的相依关系,运用核范数多目标矩阵回归的动态跟踪策略和自适应权重学习方法对不确定性环境下的投资组合进行优化配置,最终获得非线性风险叠加和高维稀疏低秩优化下资产组合的最优投资策略。研究发现,基于图网络结构的熵不确定性风险链路预测模型可以有效捕捉资产之间的非线性叠加效应和发现潜在风险点,稀疏、低秩优化组合能够高效地对高维资产进行选择,更好地集中配置优质资产,风险收益的均衡性更合理,组合性能更具优势,鲁棒性更强。实证结论对全面风险管理、量化组合分析、指数基金投资和风险资产定价具有重要指导意义。

【Abstract】 The linkage of assets has strong network characteristics, and the contagion and spread of risks has gradually evolved from a simple one-way driving relationship to a network-like cyclical interaction relationship.Incorporating the contagion and spillover of risks into the framework of optimal allocation of investment portfolios, and studying the effects of asset volatility clustering and network spreading effects of risks, can provide a new perspective and thinking for avoiding investment risks and comprehensive risk management.Sparse low-rank algorithms and graph network structure-based entropy uncertainty risk models are used to dig deeper into asset characteristics and capture the dependencies between them. Then, using the dynamic tracking strategy of kernel-norm multi-objective matrix regression and adaptive weight learning method to optimize the allocation of portfolios in uncertain environments, the portfolios under nonlinear risk superposition and sparse low-rank optimization Strategy are obtained. It is found that the uncertainty risk model based on network structure entropy can effectively capture the non-linear superposition effect between assets, and the sparse, lowrank optimized portfolio can effectively select high-dimensional assets, and better focus on the allocation of highquality assets. The income balance is more reasonable, the portfolio performance is more advantageous, and the robustness is stronger. The empirical conclusions have important guiding significance for comprehensive risk management, quantitative portfolio analysis, index fund investment, and risk asset pricing.

【基金】 国家社会科学基金项目(23FTJB003)
  • 【文献出处】 中国管理科学 ,Chinese Journal of Management Science , 编辑部邮箱 ,2024年04期
  • 【分类号】F832.51
  • 【下载频次】89
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