估计极端依赖的变化点,应用于COVID-19大流行期间的航空股价
Arnab Hazra1, Shiladitya Bose1
1Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, India.
Journal of applied statistics
|February 14, 2025
概括
这项研究分析了在COVID-19期间印度航空公司股票回报的尾部依赖度的变化,使用了双变的Hüsler-Reiss模型. 检测到与封锁阶段相关的重大变化点,有助于优化未来的流行病投资组合.
科学领域:
- 金融计量经济学 金融计量经济学
- 极端价值理论是一个极端价值理论.
- 时间序列分析时间序列分析.
背景情况:
- 尾部依赖度,用chi-measure来衡量,评估金融市场的极端协同运动.
- 随着COVID-19大流行,金融市场发生了重大结构性变化.
- 了解这些变化对于风险管理和投资组合优化至关重要.
研究的目的:
- 在COVID-19大流行期间模拟和检测IndiGo和SpiceJet每日股票回报的尾部依赖的结构变化.
- 将变化点检测方法应用于从二变的Hüsler-Reiss分布中获得的chi测量.
- 评估概率比率测试 (LRT) 和修改信息标准 (MIC) 的性能,用于变化点估计.
主要方法:
- 模拟使用双变的Hüsler-Reiss (BHR) 分布的每日最大和最小收益率 (RoR).
- 采用概率测试 (LRT) 和修改信息标准 (MIC) 来检测BHR模型的Chi测量中的变化点.
- 为LRT和MIC统计生成关键值和功率曲线.
- 进行数值模拟来评估变化点估计器的一致性.
主要成果:
- 对于航空公司股票回报,确定了尾部依赖的重大变化点.
- 检测到的变化点与锁定和解锁阶段的宣布现实一致.
- 较长距离测试和MIC方法在识别这些结构性断裂方面被证明是有效的.
结论:
- 该研究成功地确定了COVID-19大流行期间航空公司股票回报的尾部依赖性的关键转变.
- 这些发现为类似危机时期的投资组合优化策略提供了宝贵的见解.
- 应用的变化点检测方法为分析金融市场动态提供了一个强大的框架.
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