The Pernicious Effects of Contaminated Data in Risk Management
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1789928
- author
- Vilhelmsson, Anders LU ; Perignon, Christophe and Fresard, Laurent
- organization
- publishing date
- 2011
- 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
- 2016-04-01 10:15:06
- date last changed
- 2022-02-17 08:16:11
@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}},
keywords = {{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}},
doi = {{10.1016/j.jbankfin.2011.02.013}},
volume = {{35}},
year = {{2011}},
}