论文标题
异质尾部依赖的生成学习
Generative Learning of Heterogeneous Tail Dependence
论文作者
论文摘要
我们提出了一个多元生成模型，以捕获业务和财务数据中经常遇到的复杂依赖性结构。我们的模型具有各个尺寸对之间的异质和不对称的尾巴依赖性，同时还允许边缘尾部的异质性和不对称性。我们的模型结构的一个重要优点是，随着数据集的尺寸较大，参数估计过程中的错误不容易传播，因此非常可扩展。但是，由于缺乏封闭形式的密度函数，在我们的情况下，可能的参数估计是不可行的。取而代之的是，我们设计了一个新颖的时刻学习算法来学习参数。为了证明模型及其估计器的有效性，我们在模拟和现实世界数据集上对其进行了测试。结果表明，与基于Copula的基准和最新模型相比，该框架可提供更好的有限样本性能。
We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymmetric tail dependence between all pairs of individual dimensions while also allowing heterogeneity and asymmetry in the tails of the marginals. A significant merit of our model structure is that it is not prone to error propagation in the parameter estimation process, hence very scalable, as the dimensions of datasets grow large. However, the likelihood methods are infeasible for parameter estimation in our case due to the lack of a closed-form density function. Instead, we devise a novel moment learning algorithm to learn the parameters. To demonstrate the effectiveness of the model and its estimator, we test them on simulated as well as real-world datasets. Results show that this framework gives better finite-sample performance compared to the copula-based benchmarks as well as recent similar models.
