An MVAR framework to capture extreme events in macro-prudential stress tests
AbstractSevere financial turbulences are driven by high impact and low probability events that are the characteristic 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, we adopt a mixture vector autoregressive (MVAR) model framework that allows for a multi-modal distribution of the residuals. A comparison between the respective results of a VAR and MVAR approach suggests that the mixture of distributions allows for a better assessment of the effect that adverse shocks have on counterparty credit risk, the real economy and banks’ capital requirements. Consequently, we argue that the MVAR provides a more accurate assessment of risk since it captures the fat tail events often observed in time series of default probabilities. JEL Classification: C15, E44, G01, G21
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Date of creation: Aug 2012
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Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
- G01 - Financial Economics - - General - - - Financial Crises
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-09-16 (All new papers)
- NEP-BAN-2012-09-16 (Banking)
- NEP-CBA-2012-09-16 (Central Banking)
- NEP-RMG-2012-09-16 (Risk Management)
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