A mixture vector autoregressive framework to capture extreme events in macro-prudential stress tests

Abstract

Severe financial turbulence is driven by high impact and low probability events that are the hallmarks of systemic financial stress. These unlikely adverse events arise from the extreme tail of a probability distribution and are therefore very poorly captured by traditional econometric models that rely on the assumption of normality. In order to address the problem of extreme tail events in a stress testing framework, we adopt a mixture vector autoregressive (MVAR) model framework that allows for a multimodal distribution of the residuals. We use permutation tests to compare MVAR results to those of a VAR and find that mixture of distributions provides a better assessment of the impact of adverse shocks on counterparty credit risk, the real economy and banks' capital requirements. Consequently, we argue that the MVAR provides a more accurate assessment of risk owing to its ability to capture the fat tail events often observed in time series of default probabilities.