Multi-quantile systemic financial risk based on a monotone composite quantile regression neural network

This study proposes 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 robust in CoVaR estimating. In addition, 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 a systemic risk spillover network across countries in the Asia–Pacific region by considering the suffering and overflow effects. A comparison among MCQRNN , QRNN , and MCQRNN-QQ indicates the significance of monotone composite quantiles in modeling CoVaR . Additionally, the network analysis of composite risk spillovers illustrates the advantages of MCQRNN-QQ-CoVaR compared with QRNN-CoVaR . Moreover, the average composite systemic suffering index and the average composite systemic overflow index are introduced as country-specific measures that enable identifying systemically relevant countries during extreme events.