倾向性得分匹配:一种用于消费者风险建模和投资组合承保的工具
Jennifer Lewis Priestley1, Eric VonDohlen1
1Flock Specialty Finance, Data Science and Risk Management, Atlanta, GA, USA.
Journal of applied statistics
|September 13, 2024
概括
倾向性得分匹配,一种来自医学研究的统计方法,现在可以预测消费者资产的财务风险. 这种技术有助于改善当数据有限或不平衡时的投资组合承保.
科学领域:
- 金融风险建模 金融风险建模
- 统计学方法论 统计学方法论
- 消费者资产估值 消费者资产估值
背景情况:
- 金融服务依赖于经验技术进行风险和价值评估.
- 在从过去的数据中预测未来业绩时,不受控制的样本引入了偏见.
- 倾向分数匹配 (PSM) 是一种在医学和社会科学中用于处理非随机实验设计的统计方法.
研究的目的:
- 在金融风险建模和投资组合承保中展示倾向性得分匹配的实际应用.
- 从社会科学中调整一种方法,用于金融环境.
- 评估PSM对预测消费者资产风险的有用性.
主要方法:
- 将倾向性评分匹配 (PSM) 应用到一组陷入困境的消费者信贷账户组合中.
- 利用PSM解决金融风险评估中的偏见问题.
- 在有限的观察和不平衡的共变量的情况下评估PSM的有效性.
主要成果:
- 倾向性得分匹配有效预测账户级和投资组合级风险.
- 该研究验证了PSM作为金融风险建模的可行工具.
- 在有限的数据和众多潜在功能的情况下,PSM证明是有用的.
结论:
- 倾向性得分匹配应整合到金融风险建模和估值的方法工具包中.
- 对于消费者资产的承保,PSM提供了一种实际的解决方案,尤其是在数据限制的情况下.
- 这种方法提高了金融投资组合风险评估的可靠性.
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