Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions
研究用贝叶斯方法分析对偶连接函数构造（PCC），通过马尔可夫链蒙特卡洛算法估计参数并计算可信区间，应用于挪威金融收益率和欧元互换利率数据，帮助金融从业者更准确建模极端事件依赖关系。
We provide a Bayesian analysis of pair-copula constructions (PCCs) (Aas et al., 2009), which outperform many other multivariate copula constructions in modeling dependencies in financial data. We use bivariate t-copulas as building blocks in a PCC to allow extreme events in bivariate margins individually. While parameters may be estimated by maximum likelihood, confidence intervals are difficult to obtain. Consequently, we develop a Markov chain Monte Carlo (MCMC) algorithm and compute credible intervals. Standard errors obtained from MCMC output are compared to those obtained from a numerical Hessian matrix and bootstrapping. As applications, we consider Norwegian financial returns and Euro swap rates. Finally, we apply the Bayesian model selection approach of Congdon (2006) to identify conditional independence, thus constructing more parsimonious PCCs.