Value-at-Risk in turbulence time

Genest, Benoit and Cao, Zhili (2014): Value-at-Risk in turbulence time. Published in: GARP Association / Risk.net

Abstract

Value-at-Risk (VaR) has been adopted as the cornerstone and common language of risk management by virtually all major financial institutions and regulators. However, this risk measure has failed to warn the market participants during the financial crisis. In this paper, we show this failure may come from the methodology that we use to calculate VaR and not necessarily for VaR measure itself. we compare two different methods for VaR calculation, 1. by assuming the normal distribution of portfolio return, 2. by using a bootstrap method in a nonparametric framework. The Empirical exercise is implemented on CAC40 index, and the results show us that the first method will underestimate the market risk - the failure of VaR measure occurs. Yet, the second method overcomes the shortcomings of the first method and provides results that pass the tests of VaR evaluation.

• Item Type: MPRA Paper
• Language: English
• Keywords: Value-at-risk, GARCH model, Bootstrap, hit function, VaR evaluation.
• Subjects: C - Mathematical and Quantitative Methods > C0 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; G - Financial Economics > G1 - General Financial Markets

References

Acharya, V., and M. Richardson (2009): “Restoring Financial System: How to repair a Failed System,” John Wiley and sons.

Adrian, T., and M. Brunnermeirer (2011): “CoVaR,” Fedral reserve of NewYork Working paper.

Adrian, T., and H. Shin (2011): “Liquidity and Leverage,” Journal of Finan- cial Intermediation, 19, 418–437.

Artzner, P., F. Delbaen, J. Eber, and D. Heath (1997): “Thinking co- herently,” Risk, 10, 68–71.

Brunnermeier, M., and L. Pedersen (2009): “Market liquidity and funding liquidity,” Review of Financial Studies, 22.

Christofferson, P., and S. GonCalves (2005): “Estimation risk in financial risk management,” Journal of Risk, 7, 1–28.

Cont, R., R. Deguest, and G. Scandolo (2010): “Robustness and sen- sitivity analysis of risk measurement procedures,” Quantitative finance, 10, 593–606.

Engle, R. (2002): “Dynamic conditional correlation: A simple class of mul- tivariate generalized autoregressive conditional heteroskedasticity models,” Journal of Buisiness and Economic Statistics, 20, 236–249.

Gourieroux, C., and A. Monfort (2011): “Allocating Systematic and Un- systematic Risks in a Regulatory Perspective,” CREST Working paper.

Jorion, P. (2006): “Value at Risk,” McGraw-Hill, 3rd edn. Kashyap, A., and J. Stein (2004): “Cyclical Implications of the Basel II Capital Standards,” Federal Reserve Bank of Chicago Economic Perspectives, 20.

Lehar, A. (2005): “Measuring systemic risk: A risk management approach,”Journal of Banking and Finance, 29, 2577–2603.

Nieto, M., and E. Ruiz (2009): “Measuring financial risk: Comparison of alternative procedures to estimates VaR and ES,” Universidad Carlos III de Madirid Working paper. (2010): “Bootstrap prediction intervals for VAR and ES in the context of GARCH models,” Universidad Carlos III de Madirid Working paper.

Pascual, L., M. Nieto, and E. Ruiz (2006): “Bootstrap prediction for re- turns and volatilities in GARCH models,” Computational Statistics and Data Analysis, 50, 2293–2312.

Ruiz, E., and L. Pascual (2002): “Bootstrap financial time series,” Journal of Ecomonic survey, 16, 271–300.