Chang , ChiaLin and Jiménez Martín, Juan Ángel and McAleer, Michael and Pérez Amaral, Teodosio (2011) Risk Management of Risk under the Basel Accord: Forecasting ValueatRisk of VIX Futures. [Working Paper or Technical Report] (Unpublished)
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Abstract
The Basel II Accord requires that banks and other Authorized Deposittaking Institutions (ADIs) communicate their daily risk forecasts to the appropriate monetary authorities at the beginning of each trading day, using one or more risk models to measure ValueatRisk (VaR). The risk estimates of these models are used to determine capital requirements and
associated capital costs of ADIs, depending in part on the number of previous violations, whereby realised losses exceed the estimated VaR. McAleer, JimenezMartin and Perez
Amaral (2009) proposed a new approach to model selection for predicting VaR, consisting of combining alternative risk models, and comparing conservative and aggressive strategies for choosing between VaR models. This paper addresses the question of risk management of risk, namely VaR of VIX futures prices. We examine how different risk management strategies performed during the 200809 global financial crisis (GFC). We find that an aggressive strategy of choosing the Supremum of the single model forecasts is preferred to the other alternatives, and is robust during the GFC. However, this strategy implies relatively high
numbers of violations and accumulated losses, though these are admissible under the Basel II Accord.
Item Type:  Working Paper or Technical Report 

Additional Information:  JEL Classifications: G32, G11, G17, C53, C22. 
Uncontrolled Keywords:  Median strategy, ValueatRisk (VaR), daily capital charges, violation penalties, optimizing strategy, aggressive risk management, conservative risk management, Basel II Accord, VIX futures, global financial crisis (GFC). 
Subjects:  Social sciences > Economics > Finance 
Series Name:  Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE) 
Volume:  2011 
Number:  2 
ID Code:  12285 
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 ModelBased 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 ModelBased 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 valueatrisk 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. 177181. Bollerslev, T. (1986), Generalised autoregressive conditional heteroscedasticity, Journal of Econometrics, 31, 307327. 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, 196200. 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).
Chicago Board Options Exchange, (2003), VIX: CBOE volatility index, Working paper, Chicago. Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 9871007. 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 valueatrisk 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, 17791801. Huskaj, B. (2009) A ValueatRisk Analysis of VIX Futures Long Memory, Heavy Tails, and Asymmetry. Available at SSRN: http://ssrn.com/abstract=1495229.
JimenezMartin, J.A., McAleer, M. and T. PérezAmaral (2009), The Ten Commandments for managing valueatrisk under the Basel II Accord, Journal of Economic Surveys, 23, 850855.
Jorion, P. (2000), Value at Risk: The New Benchmark for Managing Financial Risk, McGrawHill, 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, 245269. Reprinted in M. McAleer and L. Oxley (eds.), Contributions to Financial Econometrics: Theoretical and Practical Issues, Blackwell, Oxford, 2002, pp. 933. Ling, S. and M. McAleer (2002a), Stationarity and the existence of moments of a family of GARCH processes, Journal of Econometrics, 106, 109117. 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, 18,722729. Ling, S. and M. McAleer, (2003a), Asymptotic theory for a vector ARMAGARCH model, Econometric Theory, 19, 278308.
Ling, S. and M. McAleer (2003b), On adaptive estimation in nonstationary ARMA models with GARCH errors, Annals of Statistics, 31, 642674. Lopez, J.A. (1999), Methods for evaluating valueatrisk estimates, Economic Review, Federal Reserve Bank of San Francisco, pp. 317. McAleer, M. (2005), Automated inference and learning in modeling financial volatility, Econometric Theory, 21, 232261. McAleer, M. (2009), The Ten Commandments for optimizing valueatrisk and daily capital charges, Journal of Economic Surveys, 23, 831849.
McAleer, M., F. Chan and D. Marinova (2007), An econometric analysis of asymmetric volatility: theory and application to patents, Journal of Econometrics, 139, 259284. McAleer, M., J.Á. JiménezMartin and T. PérezAmaral (2010a), A decision rule to minimize daily capital charges in forecasting valueatrisk, Journal of Forecasting, 29, 617634. McAleer, M., J.Á. JiménezMartin and T. PérezAmaral (2010b), Has the Basel II Accord encouraged risk management during the 200809 financial crisis?, Available at SSRN: http://ssrn.com/abstract=1397239. McAleer, M., J.Á. JiménezMartin and T. PérezAmaral (2010c), GFCrobust risk management strategies under the Basel Accord, Available at SSRN: http://ssrn.com/abstract=1688385.
McAleer, M., J.Á. JiménezMartin and T. PérezAmaral (2011) International Evidence on GFCRobust Forecasts for Risk Management Under the Basel Accord. Available at SSRN: http://ssrn.com/abstract=1741565. McAleer, M. and B. da Veiga (2008a), Forecasting valueatrisk with a parsimonious portfolio spillover GARCH (PSGARCH) model, Journal of Forecasting, 27, 119. McAleer, M. and B. da Veiga (2008b), Single index and portfolio models for forecasting valueatrisk thresholds, Journal of Forecasting, 27, 217235. McAleer, M. and C. Wiphatthanananthakul (2010), A simple expected volatility (SEV) index: Application to SET50 index options, Mathematics and Computers in Simulation, 80, 20792090. Nelson, D.B. (1991), Conditional heteroscedasticity in asset returns: a new approach, Econometrica, 59, 347370. Pérignon, C., Z.Y. Deng and Z.J. Wang (2008), Do banks overstate their valueatrisk?, Journal of Banking & Finance, 32, 783794. 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. BarndorfNielsen, D.R. Cox and D.V. Hinkley (eds.), Statistical Models in Econometrics, Finance and Other Fields, Chapman & Hall, London, 167.
Stahl, G. (1997), Three cheers, Risk, 10, pp. 6769. Whaley, R.E., 1993, Derivatives on market volatility: Hedging tools long overdue, Journal of Derivatives, 1, 7184. Zumbauch, G. (2007), A Gentle Introduction to the RM 2006 Methodology, New York, Riskmetrics Group.

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