The Pernicious Effects of Contaminated Data in Risk Management
Speaker
Abstract
| Banks hold capital to guard against unexpected surge 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. Our estimation reveals that the use of contaminated data reduces (market-risk induced) regulatory capital by around 17%. |
| Contact information: |
| Viorel Roscovan |
