Value-at-risk and the global financial crisis

Manh Ha Tran; Ngoc Mai Tran

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VaR estimates based on the simple models are more accurate than banks’ VaR according to the statistical tests.
GARCH-type models with Student’s t distribution outperform banks’ internal model in dealing with extreme losses during financial crisis.
The poor performance of bank VaR can be attributed to the choice of faulty internal VaR models.

Abstract

Using daily data for seven large international banks, we examine the forecasting ability of bank value-at-risk (VaR) estimates around the 2007–9 global financial crisis (GFC) period. We find that the banks’ internal VaR estimates are very inaccurate. They systematically overstated VaR during the pre- and postcrisis periods, with mixed performance during the GFC. Some banks inflated their VaRs, while others experienced excessive VaR exceptions and clustering. VaR estimates based on simple models of generalized autoregressive conditional heteroscedasticity (GARCH) type easily outperform internal VaR estimates. The VaR estimated via a GARCH-t distribution captures the extreme losses reasonably well. We attribute the poor VaR estimates at banks to the banks’ inappropriate choice of internal VaR models.

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