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The Pernicious Effects of Contaminated Data in Risk Management

Vilhelmsson, Anders LU ; Perignon, Christophe and Fresard, Laurent (2011) In Journal of Banking & Finance 35(10). p.2569-2583
Abstract
Banks 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... (More)
Banks 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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
proprietary trading, Regulatory capital, backtesting, value-at-risk, profit-and-loss
in
Journal of Banking & Finance
volume
35
issue
10
pages
2569 - 2583
publisher
Elsevier
external identifiers
  • wos:000294037800006
  • scopus:79960918942
ISSN
1872-6372
DOI
10.1016/j.jbankfin.2011.02.013
language
English
LU publication?
yes
id
42387884-f6eb-4d2f-a51e-f7261fc3c159 (old id 1789928)
date added to LUP
2011-02-23 09:21:00
date last changed
2017-01-29 03:06:58
@article{42387884-f6eb-4d2f-a51e-f7261fc3c159,
  abstract     = {Banks 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.},
  author       = {Vilhelmsson, Anders and Perignon, Christophe and Fresard, Laurent},
  issn         = {1872-6372},
  keyword      = {proprietary trading,Regulatory capital,backtesting,value-at-risk,profit-and-loss},
  language     = {eng},
  number       = {10},
  pages        = {2569--2583},
  publisher    = {Elsevier},
  series       = {Journal of Banking & Finance},
  title        = {The Pernicious Effects of Contaminated Data in Risk Management},
  url          = {http://dx.doi.org/10.1016/j.jbankfin.2011.02.013},
  volume       = {35},
  year         = {2011},
}