Complutense University Library

International Evidence on GFC-robust Forecasts for Risk Management under the Basel Accord

McAleer, Michael and Jiménez Martín, Juan Ángel and Pérez Amaral, Teodosio (2011) International Evidence on GFC-robust Forecasts for Risk Management under the Basel Accord. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico; nº 01, 2011, ] (Unpublished)

[img] PDF
Available under License Creative Commons Attribution.

901kB

Official URL: http://eprints.ucm.es/12020/

View download statistics for this eprint

==>>> Export to other formats

Abstract

A risk management strategy that is designed to be robust to the Global Financial Crisis
(GFC), in the sense of selecting a Value-at-Risk (VaR) forecast that combines the
forecasts of different VaR models, was proposed in McAleer et al. (2010c). The robust
forecast is based on the median of the point VaR forecasts of a set of conditional
volatility models. Such a risk management strategy is robust to the GFC in the sense
that, while maintaining the same risk management strategy before, during and after a
financial crisis, it will lead to comparatively low daily capital charges and violation
penalties for the entire period. This paper presents evidence to support the claim that the
median point forecast of VaR is generally GFC-robust. 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. In the
empirical analysis, we choose several major indexes, namely French CAC, German
DAX, US Dow Jones, UK FTSE100, Hong Kong Hang Seng, Spanish Ibex35, Japanese
Nikkei, Swiss SMI and US S&P500. The GARCH, EGARCH, GJR and Riskmetrics
models, as well as several other strategies, are used in the comparison. Backtesting is
performed on each of these indexes using the Basel II Accord regulations for 2008-10 to
examine the performance of the Median strategy in terms of the number of violations
and daily capital charges, among other criteria. The Median is shown to be a profitable
and safe strategy for risk management, both in calm and turbulent periods, as it provides
a reasonable number of violations and daily capital charges. The Median also performs
well when both total losses and the asymmetric linear tick loss function are considered


Item Type:Working Paper or Technical Report
Additional Information:

JEL Classifications: G32, G11, G17, C53, C22.

Uncontrolled Keywords:Median strategy, Value-at-Risk (VaR), Daily capital charges, Robust forecasts, Violation penalties, Optimizing strategy, Aggressive risk management, Conservative risk management, Basel II Accord, Global financial crisis (GFC).
Subjects:Social sciences > Economics > Finance
Series Name:Documentos de Trabajo del Instituto Complutense de Análisis Económico
Volume:2011
Number:01
ID Code:12020
References:

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.

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, in 1976 Proceedings of the American Statistical Association, Business & Economic Statistics Section, pp. 177-181.

Bollerslev, T. (1986), Generalised autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 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.

Caporin, M. and M. McAleer (2010a), The Ten Commandments for managing investments, Journal of Economic Surveys, 24, 196-200.

Caporin, M. and M. McAleer (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).

Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.

Franses, P.H. and D. van Dijk (1999), Nonlinear Time Series Models in Empirical Finance, Cambridge, Cambridge University Press.

Gizycki, M. and N. Hereford (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., R. Jagannathan and D. Runkle (1992), On the relation between the expected value and volatility of nominal excess return on stocks, Journal of Finance, 46, 1779- 1801.

Jimenez-Martin, J.-A., McAleer, M. and T. Pérez-Amaral (2009), The Ten Commandments for managing value-at-risk under the Basel II Accord, Journal of Economic Surveys, 23, 850-855.

Jorion, P. (2000), Value at Risk: The New Benchmark for Managing Financial Risk, McGraw-Hill, New York.

Li, W.K., S. Ling and M. McAleer (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, 2002, pp. 9-33.

Ling, S. and M. McAleer (2002a), Stationarity and the existence of moments of a family of GARCH processes, Journal of Econometrics, 106, 109-117.

Ling, S. and M. McAleer (2002b), Necessary and sufficient moment conditions for the GARCH(r,s) and asymmetric power GARCH(r,s) models, Econometric Theory, 18, 722-729.

Ling, S. and M. McAleer, (2003a), Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory, 19, 278-308.

Ling, S. and M. McAleer (2003b), On adaptive estimation in nonstationary ARMA models with GARCH errors, Annals of Statistics, 31, 642-674.

Lopez, J.A. (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, 232-261.

McAleer, M. (2009), The Ten Commandments for optimizing value-at-risk and daily capital charges, Journal of Economic Surveys, 23, 831-849.

McAleer, M., F. Chan and D. Marinova (2007), An econometric analysis of asymmetric volatility: theory and application to patents, Journal of Econometrics, 139, 259-284.

McAleer, M., J.-Á. Jiménez-Martin and T. Pérez-Amaral (2010a), A decision rule to minimize daily capital charges in forecasting value-at-risk, Journal of Forecasting, 29, 617-634.

McAleer, Michael, J-A., Jiménez-Martin and T. Perez Amaral (2010b), Has the Basel II Accord encouraged risk management during the 2008-09 financial crisis?, Available at SSRN: http://ssrn.com/abstract=1397239.

McAleer, Michael, J-A. Jiménez-Martin, Juan-Angel and T. Perez Amaral, (2010c), GFCrobust risk management strategies under the Basel Accord, Available at SSRN: http://ssrn.com/abstract=1688385.

McAleer, M. and B. da Veiga (2008a), Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model, Journal of Forecasting, 27, 1-19.

McAleer, M. and B. da Veiga (2008b), Single index and portfolio models for forecasting value-at-risk thresholds, Journal of Forecasting, 27, 217-235.

Nelson, D.B. (1991), Conditional heteroscedasticity in asset returns: a new approach, Econometrica, 59, 347-370.

Pérignon, C., Z.Y. Deng and Z.J. Wang (2008), Do banks overstate their value-at-risk?, Journal of Banking & Finance, 32, 783-794.

Riskmetrics (1996), J.P. Morgan Technical Document, 4th Edition, New York, J.P. Morgan.

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, 1-67.

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:17 Jan 2011 11:18
Last Modified:15 Nov 2013 10:52

Repository Staff Only: item control page