Generating Copula‐Correlated Random Variables: A Sequential Acceptance‐Rejection Method
提出一种序贯接受-拒绝方法生成具有给定Copula和边际分布的随机向量，避免传统方法中的逆变换，计算成本随维度线性增长，适用于时间序列数据。
ABSTRACT In this paper, we consider the problem of generating a random vector whose joint distribution is characterized by a copula function and their marginal distribution functions. We propose a sequential acceptance‐rejection method, which avoids the inversion of both marginal distributions and conditional distributions of the copula function which are needed in traditional generation methods. In our method, we first generate samples based on the marginal distributions and then use an acceptance‐rejection procedure to construct samples with the desired dependence structure. The method is also applicable in generating time series random variables. We prove that the computational cost of our method only grows linearly to their dimensionality for several types of copulas. Finally, we provide extensive numerical experiments which show our method is quite efficient in comparison with traditional methods.