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相关概念视频

Econometric Views (EViews)01:29

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First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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一个数据驱动的强大的EVaR-PC,适用于投资组合管理.

Qingyun He1, Chuanyang Hong2

  • 1School of Business Administration, The Southwestern University of Finance and Economics, Chengdu, China.

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概括
此摘要是机器生成的。

本研究引入了强大的机会受约束优化的一种新方法,它结合了分布式强大的优化和机会约束. 新的EVaR-PC近似为不确定性下决策提供了一种不那么保守而更实用的方法.

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科学领域:

  • 优化理论 优化理论
  • 决策科学 决策科学 决策科学
  • 金融数学 金融数学

背景情况:

  • 强大的机会受约束优化问题 (RCCOP) 集成分布式强大的优化 (DRO) 和机会限制 (CC) 来建模不确定性.
  • 机会约束 (CC),相当于风险价值 (VaR),通常通过风险指标来近似,例如可处理性风险价值 (EVaR) 或条件风险价值 (CVaR).
  • 分布强优化 (DRO) 通过考虑关于概率分布的部分信息而不是假设已知的真分布来处理不确定性.

研究的目的:

  • 开发一种新的近似方法,EVaR-PC,基于机会限制 (CC) 的EVaR.
  • 用瓦斯斯坦距离在基于差异的模糊性集合中评估拟议的EVaR-PC近似值.
  • 展示EVaR-PC方法在投资组合管理中的实际优势.

主要方法:

  • 开发一个新的EVaR-PC近似的机会限制 (CC).
  • 使用由瓦斯斯坦距离定义的基于差异的模糊性集对EVaR-PC的评估.
  • 在投资组合管理领域的应用和实验验证.

主要成果:

  • 在理论上,EVaR-PC近似比标准EVaR少保守.
  • 瓦瑟斯坦基于距离的模糊性集提供了强大的理论特性和实际数据利用.
  • 基于差异的模糊性集有效估计了名义分布,并减少了对先前知识的依赖.
  • 投资组合管理中的实验结果展示了EVaR-PC方法的优势.

结论:

  • 新的EVaR-PC近似为强大的机会受约束优化问题 (RCCOP) 提供了一种可处理和不那么保守的方法.
  • 使用瓦瑟斯坦距离和基于差异的模两可的集合增强了优化的实际适用性和数据驱动性.
  • 该方法在投资组合管理等应用中显示出显著的优势,在不确定性下提供了改进的决策.