高频增强的VaR:对于各种投资组合和市场状况而言,一个强大的单变量实现波动性模型
1School of Sport Business, Guangzhou Sport University, Guangzhou, Guangdong, China.
PloS one
|May 22, 2024
概括
本研究引入了一种使用高频数据的新单变量模型,用于改进风险价值 (VaR) 预测. 该模型提高了多样化的投资组合的准确性,超过了金融风险管理中的传统方法.
科学领域:
- 金融风险管理 金融风险管理
- 计量经济学 计量经济学
- 量化金融 量化金融
背景情况:
- 准确的风险价值 (VaR) 预测在金融风险管理中至关重要.
- 现有的高频单变模型主要在指数投资组合上进行测试,对于各种风险概况缺乏稳定性.
- 市场状况,特别是在危机期间,对传统的VaR模型构成挑战.
研究的目的:
- 通过使用高频率的日内数据来评估一个单变量模型,以改善投资组合VaR预测.
- 在不断变化的市场条件下,评估这些模型对具有不同风险配置文件的投资组合的稳定性.
- 为增强的VaR估计提出一个精细的单变量长内存实现波动性模型.
主要方法:
- 开发了一种精细的单变量长内存实现波动模型.
- 整合实现的差异和共变量指标,消除了对参数共变量矩阵的需求.
- 经验分析将拟议模型与传统的单变量和多变量GARCH模型进行比较.
主要成果:
- 拟议的模型在VaR预测准确性方面明显优于传统的GARCH模型.
- 在单变波动模型中高频数据集成提高了准确性,并简化了风险评估.
- 该模型有效地捕捉了波动过程中的长期依赖性.
结论:
- 精细的单变量模型为投资组合VaR估计提供了一个计算简单和有效的替代方案.
- 高频数据集成对金融风险管理策略具有变革性.
- 这项研究将学术见解与实际金融应用联系起来.
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