Application of one-factor copula with Durante generators to high-dimensional data: empirical study on stock market of China

Cheng, Yangnan Liu Jianxu Songsak Sriboonchitta

This paper investigates the performance of one-Factor copula with Durante generators (FDG copula) in high-dimensional applications. We use data with 28, 102 and 227 dimensions respectively to compare the mean absolute error in three cases. The results show that estimation error deceases as dimension increases, which means the higher the dimension, the better this model performs. Empirically, we measure the dependence between industrial sectors in Chinese stock market by FDG copulas. It is found that Machinery and equipment sector has the largest dependence coefficients with other sectors. In addition, by comparing the results before and during the COVID-19 pandemic, we find that the epidemic strengthened the connection between the Computer and Media industry and other industries. FDG copulas, tractable and flexible, suits well for high-dimensional estimation. Potential of its application to other fields remains to be discovered.