Multi-quantile systemic financial risk based on a monotone composite quantile regression neural network - Ren, Chao; Zhu, Ziyan; Zhou, Donghai (Frontiers in Physics, 2024)
This study aims to propose a novel perspective to calibrate the conditional value-at-risk (CoVaR) of countries based on the monotone composite quantile regression neural network (MCQRNN). MCQRNN can fix the "quantile crossing" problem, which is more robustness in CoVaR estimating. Besides, we extend the MCQRNN method with quantile-on-quantile (QQ), which can avoid the bias in quantile regression. Building on the estimation results, we construct the systemic risk spillover network across countries in Asia-Pacific region by considering the suffering and overflowing effects. A comparison among MCQRNN, QRNN and MCQRNN-QQ indicates the significance of monotone composite quantile in modelling CoVaR. Additionally, the network analysis of composite risk spillovers is stated to illustrate the advantages of MCQRNN-QQ-CoVaR compared with QRNN-CoVaR. Moreover, the average composite systemically suffering index and the average composite systemically overflowing index are introduced as country-specific measures, which enable to identify systemically relevant countries during the extreme events.