论文标题
潜在模型极值指数估计
Latent Model Extreme Value Index Estimation
论文作者
论文摘要
我们提出了一种新型的多元极端价值指数估计的策略。在诸如财务，多元时间序列组成部分中存在的波动性和风险等应用中，通常受到相同的潜在因素（例如美国次级危机）的驱动。为了估计潜在风险，我们采用了两阶段的程序。首先，使用潜在变量分析方法估算一组独立的潜在系列。然后，对潜在系列单独估算单变量风险措施，以评估其对整体风险的贡献。作为我们的主要理论贡献，我们得出了条件，在这种情况下，第一步对风险估计器的渐近行为的影响可以忽略不计。模拟证明了这两个I.I.D.的理论。和依赖的数据以及在财务数据中的应用程序说明了该方法在实践中提取风险的共同来源的有用性。
We propose a novel strategy for multivariate extreme value index estimation. In applications such as finance, volatility and risk present in the components of a multivariate time series are often driven by the same underlying factors, such as the subprime crisis in the US. To estimate the latent risk, we apply a two-stage procedure. First, a set of independent latent series is estimated using a method of latent variable analysis. Then, univariate risk measures are estimated individually for the latent series to assess their contribution to the overall risk. As our main theoretical contribution, we derive conditions under which the effect of the first step to the asymptotic behavior of the risk estimators is negligible. Simulations demonstrate the theory under both i.i.d. and dependent data, and an application into financial data illustrates the usefulness of the method in extracting joint sources of risk in practice.
