Macroprudential Regulation for the Chinese Banking Network System with Complete and Random Structures

Abstract

There has been little quantitative research on macro-prudential regulation for the Chinese banking system while the existing relevant research in other countries has not considered the network structure. Therefore, the present paper constructs a dynamic Chinese banking network system with complete and random structures and a quantitative model of macro-prudential regulation using four risk allocation mechanisms (Component VaR, Incremental VaR, Shapley value EL, and ΔCoVaR). Then we analyze empirically the macro-prudential regulation effect on the dynamic Chinese banking network system. The results show that the macro-prudential regulation focus on capital requirements for the Chinese banking network system is very effective in that most banks’ default probabilities have been reduced. Moreover, the regulation effect of the ΔCoVaR mechanism is the most significant and it has strong applicability because it is not affected by the two network structures. The next effective methods are Component VaR and Shapley value EL mechanisms. The last is the Incremental VaR mechanism. The Chinese banking system with random network is more stable in most years than that of the complete network. Lastly, our analysis suggests that setting up capital requirements based on each bank’s systemic risk contribution is able to promote the stability of the Chinese banking system.

Systemic Risk Arising from Shadow Banking and Sustainable Development: A Study of Wealth Management Products in China

“…Allen and Gale (2000) [58] pioneered the investigation of banking systemic risk by constructing an interbank network, where banks were represented as nodes. Subsequently, the analysis of systemic risks in banking sustainability has increasingly focused on the connectivity and concentration of interbank networks, as well as the heterogeneity and centrality of network structures [66][67][68]. Lenzu and Tedeschi (2012) [69] utilized endogenous banking signal confidence as a benchmark, demonstrating that a banking network characterized by a higher signal confidence exhibits greater strength and heightened systemic risk.…”