Santos, Paulo Araújo and Jiménez Martín, Juan Ángel and McAleer, Michael and Pérez Amaral, Teodosio (2011) GFC-Robust Risk Management Under the Basel Accord Using Extreme Value Methodologies. [Working Paper or Technical Report] (Unpublished)
Available under License Creative Commons Attribution Non-commercial.
Official URL: http://eprints.ucm.es/12968/
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.
|Item Type:||Working Paper or Technical Report|
|Additional Information:||JEL Classifications: G32, G11, G17, C53, C22.|
|Uncontrolled Keywords:||Value-at-Risk (VaR), DPOT, daily capital charges, Robust forecasts, Violation penalties, Optimizing strategy, Aggressive risk management, Conservative risk management, Basel, Global financial crisis.|
|Subjects:||Social sciences > Economics > Finance|
Social sciences > Economics > Econometrics
|Series Name:||Documentos de Trabajo del Instituto Complutense de Análisis Económico|
Araújo Santos, P. and Fraga Alves, M.I. (2010), A new class of independence tests for interval forecasts evaluation, Computational Statistics and Data Analysis, in press, doi:10.1016/j.csda2010.10.002.
Araújo Santos, P. (2010), Interval forecasts evaluation: R programs for a new independence test. Notas e Comunicações CEAUL 17/2010.
Araújo Santos, P. and Fraga Alves, M.I. (2011), Forecasting value-at-risk with a duration based POT method. Notas e Comunicações CEAUL 6/2011.
Balkema, A.A. and de Haan, L. (1974), Residual life time at great age. Annals of Probability, 2, pp. 792-804.
Basel Committee on Banking Supervision, (1988), International Convergence of Capital Measurement and Capital Standards, BIS, Basel, Switzerland.
Basel Committee on Banking Supervision, (1995), An Internal Model-Based Approach to Market Risk Capital Requirements, BIS, Basel, Switzerland.
Basel Committee on Banking Supervision, (1996), Supervisory Framework for the Use of “Backtesting” in Conjunction with the Internal Model-Based Approach to Market Risk Capital Requirements, BIS, Basel, Switzerland.
Basel Committee on Banking Supervision, (2006), International Convergence of Capital Measurement and Capital Standards, A Revised Framework Comprehensive
Version, BIS, Basel, Switzerland.
Bekiros, S.D. and Georgoutsos, D.A. (2005), Estimation of value-at-risk by extreme value and conventional methods: A comparative evaluation of their predictive performance. Journal of International Financial Markets, Institutions and Money, 15, Issue 3, pp. 2009-2028.
Berkowitz, J., Christoffersen P. and Pelletier D. (2009), Evaluating value-at-risk models with desk-level data, to appear in Management Science, published online in
Articles in Advance.
Berkowitz, J. and J. O’Brien (2001), How accurate are value-at-risk models at commercial banks?, Discussion Paper, Federal Reserve Board.
Black, F. (1976), Studies of stock market volatility changes, 1976 Proceedings of the American Statistical Association, Business and Economic Statistics Section, pp.
Bollerslev, T. (1986), Generalised autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, pp. 307-327.
Borio, C. (2008), The financial turmoil of 2007-?: A preliminary assessment and some policy considerations, BIS Working Papers No 251, Bank for International
Settlements, Basel, Switzerland.
Bystrom, H. (2004), Managing extreme risks in tranquil and volatile markets using conditional extreme value theory, International Review of Financial Analysis, 13, (2), pp. 133-152.
Caporin, M. and McAleer, M. (2010a), The Ten Commandments for managing investments, Journal of Economic Surveys, 24, pp. 196-200.
Caporin, M. and McAleer, M. (2010b), Model selection and testing of conditional and stochastic volatility models, to appear in L. Bauwens, C. Hafner and S. Laurent (eds.), Handbook on Financial Engineering and Econometrics: Volatility Models and Their Applications, Wiley, New York (Available at SSRN: http://ssrn.com/abstract=1676826).
Christoffersen P. (1998), Evaluating intervals forecasts, International Economic Review, 39, pp. 841-862.
Diebold, F.X., Schuermann, T. and Stroughair, J.D. (1998), Pitfalls and opportunities in the use of extreme value theory in risk Management, Working Paper, pp. 98-10,
Wharton School, University of Pennsylvania.
Embrechts, P., Klüppelberg, C. and Mikosch, T. (1997), Modeling extremal events for insurance and finance, Berlin, Springer.
Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, pp. 987-1007.
Engle, R.F. and Manganelli, S. (2004), CAViaR: Conditional autoregressive value-at risk by regression quantiles, Journal of Business & Economics Statistics, 22, pp.
Franses, P.H. and van Dijk, D. (1999), Nonlinear Time Series Models in Empirical Finance, Cambridge, Cambridge University Press.
Giacomini, R., and Komunjer, I. (2005), Evaluation and combination of conditional quantile forecasts, Journal of Business & Economic Statistics, 23, pp. 416-431.
Gizycki, M. and Hereford, N. (1998), Assessing the dispersion in banks’ estimates of market risk: the results of a value-at-risk survey, Discussion Paper 1, Australian
Prudential Regulation Authority.
Glosten, L., Jagannathan, R. and Runkle, D. (1992), On the relation between the expected value and volatility of nominal excess return on stocks, Journal of Finance, 46, pp. 1779-1801.
Ghorbel, A. and Trabelsi, A. (2008), Predictive performance of conditional extreme value theory in value-at-risk estimation, International Journal of Monetary Economics and Finance, 1(2), pp. 121-147.
Jimenez-Martin, J.-A., McAleer, M. and Pérez-Amaral, T. (2009), The Ten Commandments for managing value-at-risk under the Basel II Accord, Journal of Economic Surveys, 23, pp. 850-855.
Jorion, P. (2000), Value at Risk: The New Benchmark for Managing Financial Risk, McGraw-Hill, New York.
Kuester, K., Mittik, S. and Paolella, M.S., (2006), Value-at-risk prediction: A comparison of alternative strategies, Journal of Financial Econometrics, 4(1), pp. 53-89.
Kupiec, P. (1995), Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives, 3, pp. 73-84.
Li, W.K., Ling, S. and McAleer, M. (2002), Recent theoretical results for time series models with GARCH errors, Journal of Economic Surveys, 16, 245-269.
Reprinted in M. McAleer and L. Oxley (eds.), Contributions to Financial Econometrics: Theoretical and Practical Issues, Blackwell, Oxford, pp. 9-33.
Ling, S. and McAleer, M. (2002a), Stationarity and the existence of moments of a family of GARCH processes, Journal of Econometrics, 106, 1 pp. 09-117.
Ling, S. and McAleer, M. (2002b), Necessary and sufficient moment conditions for the GARCH(r,s) and asymmetric power GARCH(r, s) models, Econometric Theory, 18, pp. 722-729.
Ling, S. and McAleer, M. (2003a), Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory, 19, pp. 278-308.
Ling, S. and McAleer, M. (2003b), On adaptive estimation in nonstationary ARMA models with GARCH errors, Annals of Statistics, 31, pp. 642-674.
Lopez, A.J., (1999), Methods for evaluating value-at-risk estimates, Economic Review, Federal Reserve Bank of San Francisco, pp. 3-17.
McAleer, M. (2005), Automated inference and learning in modeling financial volatility, Econometric Theory, 21, pp. 232-261.
McAleer, M. (2009), The Ten Commandments for optimizing value-at-risk and daily capital charges, Journal of Economic Surveys, 23, pp. 831-849.
McAleer, M., Chan, F., and Marinova, D. (2007), An econometric analysis of asymmetric volatility: theory and application to patents, Journal of Econometrics,
139, pp. 259-284.
McAleer, M., Jiménez-Martin, J.-Á. and Pérez-Amaral, T. (2009), A decision rule to minimize daily capital charges in forecasting value-at-risk, Journal of Forecasting
29 (7), pp. 317-334.
McAleer, M., Jiménez-Martin, J-A., and Pérez-Amaral, T. (2010a), Has the Basel II Accord encouraged risk management during the 2008-09 financial crisis?
(Available at SSRN: http://ssrn.com/abstract=1397239) .
McAleer, M., Jiménez-Martin, J.A., and Pérez-Amaral, T. (2010b) GFC-robust risk management strategies under the Basel Accord (Available at SSRN: http://ssrn.com/abstract=1688385).
McAleer, M., Jiménez-Martin, J.-Á. and Pérez-Amaral, T. (2011) International evidence on GFC-robust forecasts for risk management under the Basel Accord (Available at SSRN: http://ssrn.com/abstract=1741565).
McAleer, M. and da Veiga, B., (2008a), Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model, Journal of Forecasting, 27, pp. 1-19.
McAleer, M. and da Veiga, B., (2008b), Single index and portfolio models for forecasting value-at-risk thresholds, Journal of Forecasting, 27, pp. 217-235.
McNeil, A.J. and Frey, R. (2000), Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach, Journal of Empirical Finance, 7, pp. 271-300.
Nelson, D.B. (1991), Conditional heteroscedasticity in asset returns: A new approach, Econometrica, 59, pp. 347-370.
Ozun, A., Cifter, A. and Yilmazer, S. (2010), Filtered extreme value theory for value-atrisk estimation: Evidence from Turkey, Journal of Risk Finance Incorporating
Balance Sheet, 11(2), pp. 164-179.
Pérignon, C., Deng, Z.Y. and Wang, Z.Y. (2008), Do banks overstate their value-atrisk?, Journal of Banking & Finance, 32, pp. 783-794.
Pickands III, J. (1975), Statistical inference using extreme value order statistics, Annals of Statistics, 3, pp. 119-131.
Riskmetrics (1996), J.P. Morgan Technical Document, 4th Edition, New York, J.P. Morgan.
R Development Core Team, (2008), R : A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-
900051-07-0, URL http://www.R-project.org.
Shephard, N. (1996), Statistical aspects of ARCH and stochastic volatility, in O.E. Barndorff-Nielsen, D.R. Cox and D.V. Hinkley (eds.), Statistical Models in
Econometrics, Finance and Other Fields, Chapman & Hall, London, pp. 1-67.
Smith, R. (1987), Estimating tails of probability distributions. Annals of Statististics, (15), pp. 1174-1207.
Stahl, G. (1997), Three cheers, Risk, 10, pp. 67-69.
Zumbauch, G. (2007), A Gentle Introduction to the RM 2006 Methodology, New York, Riskmetrics Group.
|Deposited On:||19 Jul 2011 10:49|
|Last Modified:||15 Nov 2013 10:49|
Repository Staff Only: item control page