The pernicious effects of contaminated data in risk management
AbstractBanks hold capital to guard against unexpected surges in losses and long freezes in financial markets. The minimum level of capital is set by banking regulators as a function of the banks' own estimates of their risk exposures. As a result, a great challenge for both banks and regulators is to validate internal risk models. We show that a large fraction of US and international banks uses contaminated data when testing their models. In particular, most banks validate their market risk model using profit-and-loss (P/L) data that include fees and commissions and intraday trading revenues. This practice is inconsistent with the definition of the employed market risk measure. Using both bank data and simulations, we find that data contamination has dramatic implications for model validation and can lead to the acceptance of misspecified risk models. Moreover, our estimates suggest that the use of contaminated data can significantly reduce (market-risk induced) regulatory capital.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Banking & Finance.
Volume (Year): 35 (2011)
Issue (Month): 10 (October)
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Web page: http://www.elsevier.com/locate/jbf
Regulatory capital Proprietary trading Backtesting Value-at-risk Profit-and-loss;
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- Barth,James R. & Caprio,Gerard & Levine,Ross, 2008.
"Rethinking Bank Regulation,"
Cambridge University Press, number 9780521709309, December.
- Darryll Hendricks & Beverly Hirtle, 1997. "Bank capital requirements for market risk: the internal models approach," Economic Policy Review, Federal Reserve Bank of New York, issue Dec, pages 1-12.
- Linda Allen & Anthony Saunders, 2004. "Incorporating Systemic Influences Into Risk Measurements: A Survey of the Literature," Journal of Financial Services Research, Springer, vol. 26(2), pages 161-191, October.
- DUFOUR, Jean-Marie, 2005.
"Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics,"
Cahiers de recherche
03-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
- Jean-Marie Dufour, 2005. "Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and non-standard asymptotics," CIRANO Working Papers 2005s-02, CIRANO.
- DUFOUR, Jean-Marie, 2005. "Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics," Cahiers de recherche 2005-03, Universite de Montreal, Departement de sciences economiques.
- Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
- Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
- Robert Engle & Simone Manganelli, 2000.
"CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles,"
Econometric Society World Congress 2000 Contributed Papers
0841, Econometric Society.
- Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
- Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
- Beverly J. Hirtle, 2003. "What market risk capital reporting tells us about bank risk," Economic Policy Review, Federal Reserve Bank of New York, issue Sep, pages 37-54.
- James M. O'Brien & Jeremy Berkowitz, 2007. "Estimating Bank Trading Risk. A Factor Model Approach," NBER Chapters, in: The Risks of Financial Institutions, pages 59-102 National Bureau of Economic Research, Inc.
- Cuoco, Domenico & Liu, Hong, 2006. "An analysis of VaR-based capital requirements," Journal of Financial Intermediation, Elsevier, vol. 15(3), pages 362-394, July.
- Hansen, B.E., 1992.
"Autoregressive Conditional Density Estimation,"
RCER Working Papers
322, University of Rochester - Center for Economic Research (RCER).
- Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
- Burgstahler, David & Dichev, Ilia, 1997. "Earnings management to avoid earnings decreases and losses," Journal of Accounting and Economics, Elsevier, vol. 24(1), pages 99-126, December.
- Beverly Hirtle, 2007. "Public disclosure, risk, and performance at bank holding companies," Staff Reports 293, Federal Reserve Bank of New York.
- Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
- Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
- Pérignon, Christophe & Smith, Daniel R., 2010. "Diversification and Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 55-66, January.
- Stulz, Rene M., 2008.
"Risk Management Failures: What Are They and When Do They Happen?,"
Working Paper Series
2008-18, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
- René M. Stulz, 2008. "Risk Management Failures: What Are They and When Do They Happen?," Journal of Applied Corporate Finance, Morgan Stanley, vol. 20(4), pages 39-48.
- Pritsker, Matthew, 2006. "The hidden dangers of historical simulation," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 561-582, February.
- Pérignon, Christophe & Deng, Zi Yin & Wang, Zhi Jun, 2008. "Do banks overstate their Value-at-Risk?," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 783-794, May.
- Danielsson, Jon & Jorgensen, Bjorn N. & de Vries, Casper G., 2002.
"Incentives for effective risk management,"
Journal of Banking & Finance,
Elsevier, vol. 26(7), pages 1407-1425, July.
- Ralf Sabiwalsky, 2012. "Does Basel II Pillar 3 Risk Exposure Data help to Identify Risky Banks?," SFB 649 Discussion Papers SFB649DP2012-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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