A0304
Title: An efficient quasi-random sampling for copulas
Authors: Sumin Wang - Nankai university (China) [presenting]
Abstract: An efficient method is examined for quasi-random sampling of copulas in Monte Carlo computations. Traditional methods, like conditional distribution methods (CDM), have limitations when dealing with high-dimensional or implicit copulas, which refer to those that cannot be accurately represented by existing parametric copulas. Instead, generative models, such as generative adversarial networks (GANs), are proposed to generate quasi-random samples for any copula. GANs are a type of implicit generative model used to learn the distribution of complex data, thus facilitating easy sampling. GANs are employed to learn the mapping from a uniform distribution to copulas. Once this mapping is learned, obtaining quasi-random samples from the copula only requires inputting quasi-random samples from the uniform distribution. This approach offers a more flexible method for any copula. Additionally, theoretical analysis of quasi-Monte Carlo estimators is provided based on quasi-random samples of copulas. Through simulated and practical applications, particularly in the field of risk management, the proposed method is validated, and its superiority is demonstrated over various existing methods.
