A Stochastic Dominance Approach to the Basel III DilemmaExpected Shortfall or VaR?

  1. Chang, Chia-Lin
  2. Jiménez-Martín, Juan-Ángel
  3. Maasoumi, Esfandiar
  4. McAleer, Michael
  5. Pérez Amaral, Teodosio
Revista:
Documentos de Trabajo (ICAE)

ISSN: 2341-2356

Año de publicación: 2015

Número: 16

Páginas: 1-41

Tipo: Documento de Trabajo

Otras publicaciones en: Documentos de Trabajo (ICAE)

Resumen

The Basel Committee on Banking Supervision (BCBS) (2013) recently proposed shifting the quantitative risk metrics system from Value-at-Risk (VaR) to Expected Shortfall (ES). The BCBS (2013) noted that �a number of weaknesses have been identified with using VaR for determining regulatory capital requirements, including its inability to capture tail risk� (p. 3). For this reason, the Basel Committee is considering the use of ES, which is a coherent risk measure and has already become common in the insurance industry, though not yet in the banking industry. While ES is mathematically superior to VaR in that it does not show �tail risk� and is a coherent risk measure in being subadditive, its practical implementation and large calculation requirements may pose operational challenges to financial firms. Moreover, previous empirical findings based only on means and standard deviations suggested that VaR and ES were very similar in most practical cases, while ES could be less precise because of its larger variance. In this paper we find that ES is computationally feasible using personal computers and, contrary to previous research, it is shown that there is a stochastic difference between the 97.5% ES and 99% VaR. In the Gaussian case, they are similar but not equal, while in other cases they can differ substantially: in fat-tailed conditional distributions, on the one hand, 97.5%-ES would imply higher risk forecasts, while on the other, it provides a smaller down-side risk than using the 99%-VaR. It is found that the empirical results in the paper generally support the proposals of the Basel Committee.

Referencias bibliográficas

  • Alexander, C. (2009), Market Risk Analysis: Value-at-Risk Models, Volume 4, Wiley, New York.
  • Artzner, P., F. Delbaen, J.M.Eber, and D. Heath (1997), Thinking coherently, Risk 10(11), 68–71.
  • Barrett, G., and S. Donald (2003), Consistent tests for stochastic dominance, Econometrica, 71, 71-104.
  • Basel Committee on Banking Supervision (2013), Consultative Document, Fundamental Review of the Trading Book: A revised Market Risk framework, BIS, Basel, Switzerland, http://www.bis.org/publ/bcbs265.pdf
  • 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 heteroskedasticity, Journal of Econometrics, 31, 307-327.
  • Caporin, M. and M. McAleer (2012),Model selection and testing of conditional and stochastic volatility models, in L. Bauwens, C. Hafner and S. Laurent (eds.), Handbook on Financial Engineering and Econometrics: Volatility Models and Their Applications, Wiley, New York, pp. 199-222.
  • Carlstein, E. (1992). Resampling Techniques for Stationary Time-Series: Some Recent Developments. New Directions in time series Analysis, 75–85.
  • Chang, C.-L., J-A. Jimenez-Martin, E. Maasoumi, and T. Pérez-Amaral (2015), A stochastic dominance approach to financial risk management strategies, to appear in Journal of Econometrics (http://www.sciencedirect.com/science/article/pii/S0304407615000573).
  • Chang, C.-L., J-A. Jimenez-Martin, M. McAleer, and T. Pérez-Amaral (2011), Risk management of risk under the Basel Accord: Forecasting value-at-risk of VIX futures, Managerial Finance, 37, 1088-1106.
  • Danielsson, J. (2013), “The new market regulations”, VoxEU.org, 28 November 2013, http://www.voxeu.org/article/new-market-risk-regulations.
  • Donald, S. and Y. Hsu (2013), Improving the power of tests of stochastic dominance. Mimeo. http://yuchinhsu.yolasite.com/resources/papers/powerful_SD.pdf
  • Dowd, K. (2005), Measuring Market Risk. Wiley, New York, 2nd edition.
  • Engle, R.F. (1982), Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.
  • Fama, E. F. and R. Roll (1968), Some properties of Symmetric Stable Distributions,” Journal of the American Statistical Association, 63, 817-36.
  • Fama, E. F. and R. Roll (1971), Parameter estimates for symmetric stable distributions, Journal of the American Statistical Asssociation, 66, 331-38.
  • Feller, W. (1969), An Introduction to Probability Theory and Its Applications, Volume 2, John Wiley and Sons.
  • Franses, P.H. and D. van Dijk (1999), Nonlinear Time Series Models in Empirical Finance, Cambridge, Cambridge University Press.
  • 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.
  • Hansen, P.R. (2005), A test for superior predictive ability, Journal of Business and Economic Statistics, 23, 365-380.
  • 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, 9-33.
  • Ling, S. and M. McAleer (2002a),Stationariety 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.
  • Linton, O., E. Maasoumi and Y.J. Whang (2005). Consistent testing for stochastic dominance under general sampling schemes. Review of Economic Studies, 72, 735-765.
  • McAleer, M. (2005), Automated inference and learning in modelling financial volatility, Econometric Theory, 21, 232-261.
  • McAleer, M. (2014), Asymmentry and leverage in conditional volatility models, Econometrics, 2(3), 145-150.
  • 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. and C. Hafner (2014), A one line derivation of EGARCH, Econometrics, 2(2), 92-97.
  • Nelson, D.B. (1991), Conditional heteroskedasticity in asset returns: a new approach, Econometrica, 59, 347-370.
  • Politis, D.N. and J.P. Romano (1992), A Circular Block-resampling Procedure for Stationary Data,in R. Lepage and L. Billard (eds.), Exploring the Limits of Bootstrap, Wiley, New York, 263–270.
  • Sheppard, K. (2013), MFE Toolbox, https://www.kevinsheppard.com/MFE_Toolbox, 7 June, 2013.
  • Shiryaev, A.N. (1999), Essentials of Stochastic Finance, Facts, Models, Theory, World Scientific.
  • Tsay, R.S. (1987), Conditional heteroskedastic time series models, Journal of the American Statistical Association, 82, 590-604.
  • Yamai, Y. and T. Yoshiba (2002), Comparative analyses of expected shortfall and Value-atRisk: Their estimation error, descomposition and optimization, Monetary and Economic Studies, 20, 2, Bank of Japan.
  • Yamai, Y. and T. Yoshiba (2005), Value-at-Risk versus Expected Shortfall: A practical perspective, Jounal of Banking and Finance, 29(4), 997-1015.