Credit contagion in the presence of non-normal shocks

- Empirically testable generalisation of the credit loss distributions.
- Maximum likelihood estimation of the credit loss distribution parameters using proprietary default data for six sectors from the Bank of Mexico through the specification of non-normality for the unobservable common factors using Skew-Normal and Skew-t densities.
- Found non-trivial contagion effects in three sectors, 'Commerce', 'Services' and 'Transport', out of which the joint presence of contagion effects with non-Gaussian shocks appears in one sector (Services).
- Documented significant impact on the Value-at-Risk levels of the 'Services' credit loss distribution
- Result suggests that traditional Basel and vendor-based credit risk models are inadequate.

We generalize existing structural credit risk models that account for contagion effects across economic sectors, to capture the impact of neglected skewness and excess kurtosis in the asset return process, on the shape of the credit loss distribution. We specify Skew-Normal and Skew-Student t densities for the underlying asset return process and estimate the derived credit loss density using sector default rates based on proprietary data from the Central Bank of Mexico for six firm sectors. We show that, out of the six sectors analyzed, there is a significant contagion effect in 'Commerce', 'Services' and 'Transport'. Moreover, we show that the non-Gaussian modelling of the common factor provides a better characterization than its Gaussian counterpart for the 'Services' sector. This result has a significant impact on the shape and the corresponding Value-at-Risk levels of the 'Services' credit loss distribution. In this context, traditional Basel and vendor-based credit risk models are inadequate as these do not consider the individual or the joint impact of contagion and non-Gaussian asset returns.