An MVAR Framework to Capture Extreme Events in Macroprudential Stress Tests

The stress testing literature abounds with reduced-form macroeconomic models that are used to forecast the evolution of the macroeconomic environment in the context of a stress testing exercise. These models permit supervisors to estimate counterparty risk under both baseline and adverse scenarios. However, the large majority of these models are founded on the assumption of normality of the innovation series. While this assumption renders the model tractable, it fails to capture the observed frequency of distant tail events that represent the hallmark of systemic financial stress. Consequently, these kinds of macro models tend to underestimate the actual level of credit risk. This also leads to an inaccurate assessment of the degree of systemic risk inherent in the financial sector. Clearly this may have significant implications for macro-prudential policy makers. One possible way to overcome such a limitation is to introduce a mixture of distributions model in order to better capture the potential for extreme events. Based on the methodology developed by Fong, Li, Yau and Wong (2007), we have incorporated a macroeconomic model based on a mixture vector autoregression (MVAR) into the stress testing framework of Rouabah and Theal (2010) that is used at the Banque centrale du Luxembourg. This allows the counterparty credit risk model to better capture extreme tail events in comparison to models based on assuming normality of the distributions underlying the macro models. We believe this approach facilitates a more accurate assessment of credit risk.