We consider several time series, and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modelled by a regime‐switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness‐of‐fit procedure based on Cramér–von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put‐on‐max option on the returns of two assets. To facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN. The Canadian Journal of Statistics 48: 79–96; 2020 © 2020 Statistical Society of Canada

中文翻译：

---

政权转换copula模型的拟合优度与期权定价中的应用

我们考虑几个时间序列，对于每个时间序列，我们都适合一个合适的动态参数模型。这将为每个时间序列产生序列独立的误差项。这些误差项之间的依赖关系然后由一个政权转换的copula来建模。EM算法用于估计参数，并提出了基于Cramér-vonMises统计的顺序拟合优度程序来选择适当数量的方案。进行数值实验以评估所提出方法的有效性。作为应用示例，我们对两种资产的收益评估了欧洲最大期权。为了方便使用我们的方法，我们构建了R包HMMcopula，可在CRAN上使用。加拿大统计杂志48：79-96；2020©2020加拿大统计学会