For modeling multivariate financial time series a single factor copula model with stochastic volatility margins is proposed. It generalizes single factor models based on the multivariate normal distribution by allowing for symmetric and asymmetric tail dependence. A joint Bayesian approach using Hamiltonian Monte Carlo (HMC) within Gibbs sampling is developed. Thus, the information loss caused by the two-step approach for margins and dependence is avoided. Further, the Bayesian approach is tractable in high dimensional parameter spaces in addition to uncertainty quantification through credible intervals. By introducing indicators for different copula families the copula families are selected automatically in the Bayesian framework. In a first simulation study the performance of HMC for the copula part is compared to a procedure based on adaptive rejection Metropolis sampling within Gibbs sampling. It is shown that HMC considerably outperforms this alternative approach in terms of effective sample size per minute. In a second simulation study satisfactory performance is seen for the full HMC within Gibbs procedure. The approach is illustrated for a portfolio of financial assets with respect to one-day ahead value at risk forecasts. Comparison to a two-step estimation procedure and to relevant benchmark models, such as a multivariate factor stochastic volatility model, shows superior performance of the proposed approach.

为了对多元金融时间序列建模，提出了具有随机波动幅度的单因子 copula 模型。它通过允许对称和非对称尾部依赖来概括基于多元正态分布的单因子模型。开发了一种在吉布斯采样中使用哈密顿蒙特卡罗 (HMC) 的联合贝叶斯方法。因此，避免了由边距和依赖性两步法引起的信息丢失。此外，除了通过可信区间进行不确定性量化之外，贝叶斯方法在高维参数空间中也易于处理。通过为不同的copula族引入指标，在贝叶斯框架中自动选择copula族。在第一个模拟研究中，将 HMC 的 copula 部分的性能与基于 Gibbs 采样内的自适应拒绝 Metropolis 采样的过程进行了比较。结果表明，HMC 在每分钟有效样本大小方面明显优于这种替代方法。在第二个模拟研究中，Gibbs 程序中的完整 HMC 具有令人满意的性能。该方法针对金融资产组合的未来风险预测价值进行了说明。与两步估计程序和相关基准模型（如多变量因子随机波动率模型）的比较显示了所提出方法的优越性能。结果表明，HMC 在每分钟有效样本大小方面明显优于这种替代方法。在第二个模拟研究中，Gibbs 程序中的完整 HMC 具有令人满意的性能。该方法针对金融资产组合的未来风险预测价值进行了说明。与两步估计程序和相关基准模型（如多变量因子随机波动率模型）的比较显示了所提出方法的优越性能。结果表明，HMC 在每分钟有效样本大小方面明显优于这种替代方法。在第二个模拟研究中，Gibbs 程序中的完整 HMC 具有令人满意的性能。该方法针对金融资产组合的未来风险预测价值进行了说明。与两步估计程序和相关基准模型（如多变量因子随机波动率模型）的比较显示了所提出方法的优越性能。