Semiparametric Dynamic Max-Copula Model for Multivariate Time Series
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AI 根据所提供的论文材料归类：摘要明确研究多元时间序列、联合尾部依赖，并应用于股票收益的风险价值估计和金融风险管理。
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多元时间序列的半参数动态 max-copula 模型
- 研究问题
- 如何为高维多元时间序列构造兼顾非对称依赖、联合尾部与非极端行为的灵活依赖模型，并用于金融风险管理？
- 方法与思路
- 作者以成对取最大规则组合现有 copula，构造单因子及分块因子形式，结合半参数时间序列模型并采用复合最大似然估计。
- 材料陈述的贡献
- 摘要称，新模型具有闭式表达和可解释性，估计量满足一致性与渐近正态性，并以数值实验及金融应用展示表现。
- 阅读关注点
- 应核对取最大构造如何保证合法 copula、估计理论的相依条件，以及风险价值和组合应用中的比较基准与评价设计。
- 阅读边界
- 仅依据所给 Crossref 摘要；摘要未列出估计条件、误差数值或应用比较细节，不能据此确认其在所有市场状态下均优于其他模型。
原文摘要
Summary The paper presents a novel non-linear framework for the construction of flexible multivariate dependence structure (i.e. copulas) from existing copulas based on a straightforward ‘pairwise max-’rule. The newly constructed max-copula has a closed form and has strong interpretability. Compared with the classical ‘linear symmetric’ mixture copula, the max-copula can be viewed as a ‘non-linear asymmetric’ framework. It is capable of modelling asymmetric dependence and joint tail behaviour while also offering good performance in non-extremal behaviour modelling. Max-copulas that are based on single-factor and block factor models are developed to offer parsimonious modelling for structured dependence, especially in high dimensional applications. Combined with semiparametric time series models, the max-copula can be used to develop flexible and accurate models for multivariate time series. A new semiparametric composite maximum likelihood method is proposed for parameter estimation, where the consistency and asymptotic normality of estimators are established. The flexibility of the max-copula and the accuracy of the proposed estimation procedure are illustrated through extensive numerical experiments. Real data applications in value-at-risk estimation and portfolio optimization for financial risk management demonstrate the max-copula's promising ability to capture accurately joint movements of high dimensional multivariate stock returns under both normal and crisis regimes of the financial market.