标题： 使用Dvine SCAR模型建模高维时变依赖关系 显示英文标题

标题： Modeling high dimensional time-varying dependence using D-vine SCAR models

摘要： 我们研究了建模多个时间序列之间依赖关系的问题。通过结合对偶copula构造（PCC）与随机自回归copula（SCAR）模型，我们构建了高维时变copula模型以捕捉随时间变化的依赖性。我们展示了如何将这个高度复杂的模型估计问题分解为一系列二元SCAR模型的估计问题，并且可以通过使用模拟最大似然法来实现。此外，通过限制PCC更高层级上的条件依赖参数为常数，我们可以在不损失太多灵活性的情况下大大减少需要估计的参数数量。我们通过大规模蒙特卡洛模拟研究了我们的估计方法的表现。对DAX 30指数所有成分股股票收益率的大规模数据集的应用展示了所提出模型类别的实用性。 显示英文摘要

摘要： We consider the problem of modeling the dependence among many time series. We build high dimensional time-varying copula models by combining pair-copula constructions (PCC) with stochastic autoregressive copula (SCAR) models to capture dependence that changes over time. We show how the estimation of this highly complex model can be broken down into the estimation of a sequence of bivariate SCAR models, which can be achieved by using the method of simulated maximum likelihood. Further, by restricting the conditional dependence parameter on higher cascades of the PCC to be constant, we can greatly reduce the number of parameters to be estimated without losing much flexibility. We study the performance of our estimation method by a large scale Monte Carlo simulation. An application to a large dataset of stock returns of all constituents of the Dax 30 illustrates the usefulness of the proposed model class.