Modeling high dimensional time-varying dependence using D-vine SCAR models
Modeling high dimensional time-varying dependence using D-vine SCAR models
复制标题
使用 D-vine SCAR 模型对高维时变依赖性进行建模
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
H. Manner
中科院分区：
文献类型：
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作者：
Carlos Almeida;C. Czado;H. Manner
本文研究了多个时间序列之间的相关性建模问题。我们建立了高维时变copula模型，通过结合配对copula结构（PCC）和随机自回归copula（SCAR）模型来捕捉随时间变化的依赖关系。我们展示了如何将这个高度复杂的模型的估计分解为一系列二元SCAR模型的估计，这可以通过使用模拟最大似然方法来实现。此外，通过限制PCC的高级联上的浓度依赖参数为常数，我们可以大大减少要估计的参数的数量，而不会损失太多的可伸缩性。我们通过大规模的Monte Carlo模拟研究了我们的估计方法的性能。一个大的数据集ofstock returns的所有成分的DAX 30的应用程序说明了有用的建议model class.Keywords：Stock returns dependence，time-varying copula，D-vines，efficient importance sampling，sequential estimationJEL分类：C15，C51，C581.多元分布的建模是风险管理和资产配置问题的重要研究内容。由于金融资产的条件均值建模是相当困难的，如果不是不可能的，许多研究都集中在建模条件波动性和依赖性。关于多元GARCH（Bauwens et al.2006）和随机波动模型（Harvey et al.1994，Yu and Meyer 2006）的文献提供了许多将单变量波动模型扩展到多元波动模型的方法
AbstractWe consider the problem of modeling the dependence among many time series. We build high dimensionaltime-varying copula models by combining pair-copula constructions (PCC) with stochastic autoregressivecopula (SCAR) models to capture dependence that changes overtime. We show how the estimation of thishighly 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 theconditional dependence parameter on higher cascades of the PCC to be constant, we can greatly reducethe number of parameters to be estimated without losing much flexibility. We study the performanceof our estimation method by a large scale Monte Carlo simulation. An application to a large dataset ofstock returns of all constituents of the Dax 30 illustrates the usefulness of the proposed model class.Keywords: Stock return dependence, time-varying copula, D-vines, efficient importance sampling,sequential estimationJEL Classification: C15, C51, C581. IntroductionThe modeling of multivariate distributions is an important task for risk management and asset al-location problems. Since modeling the conditional mean of financial assets is rather difficult, if notimpossible, much research has focused on modeling conditional volatilities and dependencies. The lit-erature on multivariate GARCH (Bauwens et al. 2006) and stochastic volatility models (Harvey et al.1994, Yu and Meyer 2006) offers many approaches to extend univariate volatility models to multivariate