Macrofinancial determinants of default probability using copula: A case study of Indonesian banks

In the aftermath of the global financial crisis of 2008, macrofinancial linkages have gained more attention from policymakers as primary issues of financial system stability. A clearer understanding of probability of default (PD) drivers may help predict if a bank will default on its portfolio liabilities. This presentation develops a method to assess a bank's PD based on a multivariate copula distribution to capture nonlinear relationships between variables with complex data structures. Then we use the generalized method of moments (GMM) to observe the relationship between PD to bank performance (bank-specific indicators) and the macroeconomic indicators. Our findings illustrate some critical links between PD and macroeconomic environments. For example, empirical evidence suggests that bank-specific indicators such as the CET 1 ratio, inefficiency ratio, and deposit ratio appear to be negatively and statistically significant to a bank's PD. When we examined the structural and macroeconomic variables, we found that the policy rate, the real exchange rate, economic growth, and the unemployment rate may reduce the PD. We also found that central state-owned banks tend to have a higher risk than other bank groups and that regional state-owned banks in the central region have the greatest likelihood of default.