An evaluation of quantitative standards for the implementation of internal market risk model by commercial banks.
Abstract
The purpose of this paper is to evaluate the quantitative standards laid down under the second Basel Accords for the implementation of internal market risk models by banks. The paper surveys available research to evaluate the standards. The standards don’t prescribe a VaR method despite evidence that volatility of financial returns is conditional and financial returns are fat tailed. The requirement of a minimum historical period also runs contrary to the finding that volatility is time varying and clustered resulting in banks being able to use weighting schemes conservatively only. The minimum horizon of ten days requires use of a scaling rule that is not accurate. The 99% confidence level requirement increases the inaccuracy when using a normal assumption on fat tailed data. The minimum updation period and minimum historical period requirements effectively smooth the market risk charge over and above the smoothing by the requirement of averaging VaR resulting in unresponsive market risk charges. The regulatory back testing framework is based on unconditional coverage and does not penalize clustered VaR exceptions.
Downloads
References
Basel Committee on Banking Supervision (2009) Revisions to the Basel II market risk framework. [Online] Available: http://www.bis.org/publ/bcbs158.pdf (November 19, 2011).
Basel Committee on Banking Supervision (2011) Messages from the Academic Literature on Risk Measurement for the Trading Book. Working Paper No. 19, [Online] Available: http://www.bis.org/publ/bcbs_wp19.pdf (November 01, 2011).
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics. 31(3), 307-328.
Boudoukh, J, M. Richardson, and R. Whitelaw (1998). The Best of Both Worlds. Risk. May, 64-67.
Campbell, S.D. (2007). A Review of Backtesting and Backtesting Procedures, The Journal of Risk. 9, 2, 1-17. Christoffersen, P and F Diebold. (2000). How relevant is volatility forecasting for financial risk management? The Review of Economics and Statistics. 82(1), 12–22.
Christoffersen, P. (1998). Evaluating interval forecasts. International Economic Review, 39: 841-62.
Diebold, Francis X., Andrew Hickman, Atsushi Inoue and Til Schuermann. (1996). Converting 1-Day Volatility to h-Day Volatility: Scaling by √h is Worse than You Think. Wharton Financial Institutions Center, Working Paper No. 97-34.
Engle, R.F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1008.
Engle, R.F. (2001). GARCH 101: The use of ARCH/GARCH models in Applied Econometrics. Journal of Economic Perspectives. 15(4), 157-168.
Hendricks, D. (1996). Evaluation of Value-at-Risk Models Using Historical Data. FRBNY Economic Policy Review. 39-69.
Hull, J. and A. White (1998). Value at Risk when Daily Changes in Market Variables are not Normally Distributed. Journal of Derivatives. Spring 1998, 9-19.
Jackson, P., Maude, D. and Perraudin, W. (1997). Bank Capital and Value at Risk. Journal of Derivatives. 4 (Spring), 73–89.
Jorion, P. (2002a). Fallacies about the effects of market risk management systems. Financial Stability Review,.December, 115-127.
Kalvyas, L. Dritsakis, N., Siriopoulos C. and Grose, C. (2004). Selecting Value at Risk Methods According to their Hidden Characteristics. Operational Research. 4(2), 167-189.
Kupiec, Paul H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. Board of Governors of the Federal Reserve System, Mimeographed.
Ouyang, Z. (2009). Model Choice and Value at Risk Estimation. Qual Quant. 43,983-991.
Perignon, C., & Smith, D. (2006). The Level and Quality of Value-at-Risk Disclosure by Commercial Banks. [Online] Available: http://www.unifr.ch/controlling/seminar/2007-2008/Perignon_ParisII.pdf, (October 28, 2011).
Poon, S. H. and C. Granger (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature. 41, 478-539.
Provizionatou, V., S. Markose and O. Menkens (2005). Empirical scaling rules for value-at-risk. [Online] Available: http://www.econ.ku.dk/fru/conference/Programme/friday/a4/provizionatou_empirical%20scaling%20rule.pdf, (October 11, 2011).
Senior Supervisors Group (2008). Observations on Risk Management Practices during the Recent Market Turbulence, New York Federal Reserve. [Online] Available: http://newyorkfed.org/newsevents/news/banking/2008/SSG_Risk_Mgt_doc_final.pdf, (October 15, 2011).
Sharma, M. (2012). The Historical Simulation Method for Value-at-Risk: A Research Based Evaluation of the Industry Favorite. [Online] Available: http://dx.doi.org/10.2139/ssrn.2042594 (April 19, 2012).
Sheedy, E. (2008). Why VaR Models Fail and what can be done. Macquarie University Applied Finance Center Research Paper no. 34.
Transition to the Internal Models Approach for Market Risk – a Survey Report (2000). Aptivaa. [Online] Available: www.aptivaa.com/pdfs/SurveyReport.pdf, August 23, 2011.
Vlaar P. (2000). Value at risk models for Dutch bond portfolios. Journal of Banking and Finance. 24, 131–154
Copyright (c) 2018 IJRDO - Journal of Business Management (ISSN: 2455-6661)
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the IJRDO Journal will have the full right to remove the published article on any misconduct found in the published article.