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GFC-Robust Risk Management Under the Basel Accord Using Extreme Value Methodologies

In McAleer et al. (2010b), a robust risk management strategy to the Global Financial Crisis (GFC) was proposed under the Basel II Accord by selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast was based on the median of the point VaR forecasts of a set of conditional volatility models. In this paper we provide further evidence on the suitability of the median as a GFC-robust strategy by using an additional set of new extreme value forecasting models and by extending the sample period for comparison. These extreme value models include DPOT and Conditional EVT. Such models might be expected to be useful in explaining financial data, especially in the presence of extreme shocks that arise during a GFC. Our empirical results confirm that the median remains GFC-robust even in the presence of these new extreme value models. This is illustrated by using the S&P500 index before, during and after the 2008-09 GFC. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria, including several tests for independence of the violations. The strategy based on the median, or more generally, on combined forecasts of single models, is straightforward to incorporate into existing computer software packages that are used by banks and other financial institutions.

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File URL: http://eprints.ucm.es/12968/1/1127.pdf
File Function: july 2011
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Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico in its series Documentos de Trabajo del ICAE with number 2011-27.

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Length: 33 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:ucm:doicae:1127
Note: The authors are most grateful for the helpful comments and suggestions of participants at the International Conference on Risk Modelling and Management, Madrid, Spain, June 2011. For financial support, the third author wishes to thank the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science. The second and fourth authors acknowledge the financial support of the Ministerio de Ciencia y Tecnología and Comunidad de Madrid, Spain.
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