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Updated: Mar 18, 2026

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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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一个受约束的强大的马尔科夫转换模式,用于长期风险评估
Shanshan Qin1, Beibei Guo2, Yuehua Wu3
1School of Statistics, Tianjin University of Finance and Economics, Tianjin, People's Republic of China.
Journal of applied statistics
|March 16, 2026
概括
本研究引入了一个受约束的强大的马尔科夫转换模式 (CRMRS) 模型,以改进股权回报分析. 该CRMRS模型提供稳定的参数估计和更好的风险评估金融资产.
科学领域:
- 量化金融 量化金融
- 计量经济学 计量经济学
- 时间序列分析时间序列分析
背景情况:
- 标准马尔科夫转换模式 (MRS) 模型在长期股票回报中与平均逆转作斗争.
- 在MRS模型中的正常性假设导致不稳定的参数估计和妥协风险评估.
- 现有的模型不足以捕捉股票回报的分配性质.
研究的目的:
- 为增强的股权回报时间序列分析提出一个受约束的强大的马尔科夫转换 (CRMRS) 模型.
- 改进金融时间序列中平均逆转和分布灵活性的建模.
- 提高投资资产风险曝光度衡量的准确性.
主要方法:
- 开发了一个CRMRS模型,其中包含了订单限制和对制度平均值和过渡概率的稀疏约束.
- 采用基于一般 ρ 的最不有利分布来提高分配灵活性.
- 使用S&P/TSX综合指数月度回报进行了有限样本模拟和经验验证.
主要成果:
- 在CRMRS-Huber模型中,在各种场景中展示了稳定的参数估计.
- 取得了更高阶时刻的优异近似,例如斜度和曲度.
- 提供了平衡的中间风险评估,性能优于标准MRS模型.
结论:
- 拟议的CRMRS模型提高了对股权回报时间序列的模型充分性.
- 与传统的MRS模型相比,CRMRS提高了风险暴露测量的准确性.
- 这种方法为金融时间序列分析和风险管理提供了更强大的框架.
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