Credit and market risks measurement in carbon financing for Chinese banks

Abstract

This paper concerns the risk analysis of six Chinese banks which are involved in carbon financing. Factor copula is introduced to simulate the corresponding carbon finance credit risk and market risk by latent variables in an indirect method. In short, the four common factors in carbon financing – exchange rates, interest rates, CER price, and Brent oil prices – are analyzed and explored in factor copula approach that incorporates KMV, GARCH models in two steps. The KMV and GARCH models are used to generate data that reflects the overall credit and market risk associated with each bank. Both normal copula and t-copula functions are used to simulate the parameter estimations for comparison in a new way. The value at risk for each of the six banks is calculated through a Monte Carlo simulation and compared. Additionally, we calculate shock estimates for each factor to explore the changes in both credit risk and market risk given economic shocks, also make a hypothetical analysis of the impact of the financial crisis on common factors. Overall, our findings reveal that exchange rates and oil prices are the key factors to consider in carbon financing. Ping An Bank is facing the most risk of all the banks in the sample, while the Industrial and Commercial Bank of China is carrying the least risk. The results of this analysis gave provide some insight into domestic carbon trading and carbon markets connectivity.