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对负倾斜的保险索赔数据的损失大小模型:应用,使用不同的方法进行风险分析和统计预测
Heba Soltan Mohamed1, Gauss M Cordeiro2, R Minkah3
1Department of Statistics and Quantitative Methods, Faculty of Business Administration, Horus University, Damietta, Egypt.
Journal of applied statistics
|February 14, 2024
概括
这项研究引入了保险索赔的新损失大小分布,改善了未来风险预测. 自动回归模型有效地估计了未来的预期索赔,帮助保险公司减轻损失.
科学领域:
- 精算科学 精算科学
- 量化金融 量化金融
- 风险管理 风险管理
背景情况:
- 准确估计未来的保险索赔对于金融稳定和在不确定性条件下防止损失至关重要.
- 保险索赔数据经常表现出负倾斜,需要专门的分布模型.
- 关键风险指标对于量化和管理潜在的金融风险是必不可少的.
研究的目的:
- 定义一个针对负偏差保险索赔数据量身定制的新型损失大小分布.
- 使用多种估计方法分析关键风险指标.
- 提出和验证一个自动回归模型来预测未来的预期索赔.
主要方法:
- 定义一个新的损失大小分配.
- 通过最大概率,普通最小方程,加权最小方程和安德森·达林估计来分析四个关键风险指标.
- 用竞争性统计模型和九个假设测试来建模和比较保险索赔数据.
- 应用自回归模型用于未来预期索赔估计,结合风险价值和峰值超过随机值的平均顺序-p方法.
主要成果:
- 对于负倾斜的保险索赔,成功定义了一个新的损失大小分布.
- 自动回归模型在分析保险索赔数据和估计未来预期价值方面表现出有效性.
- 使用统计测试进行全面的模型比较,为不同的分析方法的性能提供了洞察力.
结论:
- 建议的损失大小分布和自动回归模型为管理保险索赔不确定性提供了改进的方法.
- 准确估计未来的索赔对于保险公司来说至关重要,以减轻潜在的损失.
- 该研究为保险行业的风险评估和财务规划提供了坚实的框架.
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