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在复杂金融系统的不确定性下,对投资组合优化的高阶时刻进行非线性收缩估计
1School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China.
Entropy (Basel, Switzerland)
|October 28, 2025
概括
本研究引入了在复杂的金融模型中估计高阶时刻的非线性收缩,大大减少了估计错误并改善了投资组合性能,特别是在大型资产领域.
科学领域:
- 量化金融 量化金融
- 统计建模 统计建模
- 计量经济学 计量经济学
背景情况:
- 高维财务数据在准确估计高阶动量矩阵方面存在挑战.
- 传统的方法,如样本估计和线性收缩,与维度的诅咒作斗争.
研究的目的:
- 开发和验证一个非线性收缩估计方法,用于更高阶矩阵.
- 证明该方法在减轻估计不确定性和改善投资组合构建方面的有效性.
主要方法:
- 在多因素模型中开发一个非线性收缩估计器,用于高阶时刻.
- 对于高维设置而建立的非对称一致性.
- 蒙特卡洛模拟和经验性投资组合分析用于验证.
主要成果:
- 非线性收缩显著降低了平均平方误差 (MSE),并改善了共变性和可库尔托斯平均损失 (PRIAL) 的百分比相对改善.
- 与样本估计相比,在减轻协变性,coskewness和cokurtosis的不确定性方面取得了实质性的收益.
- 优于大型资产领域的线性收缩和样本估计器,产生更高的回报率和较低风险指标的夏普比率.
结论:
- 非线性收缩有效地减少了高阶时刻估计的不确定性.
- 该方法提高了复杂金融系统的投资组合绩效和弹性.
- 适用于各种不同的投资环境,性能与较小的宇宙中的线性收缩相提并论.
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