The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss  in the sense of a fair, unbiased estimate of risk expenses however is barely covered. So far there are no established backtesting procedures for EL, quantifying its impact with regards to pricing or risk adjusted profitability measures , such as RAROC or EVA . In this paper, a practically oriented, top-down approach to assess the quality of EL by backtesting with actually observed risk impact on capital is introduced. In a first step, the concept of risk expenses (Cost of Risk) has to be extended beyond the classical provisioning (P L) view, towards a more adequate capital consumption approach (Impact of Risk, IoR). On this basis, the difference between parameter-based EL and actually reported Impact of Risk is decomposed into its key components (PL Backtest and NPL Backtest) . The proposed method will deepen the understanding of practical properties of EL, aligns the EL with actually observed risk impact on capital and has the potential to improve the quality of EL-based business decisions. Besides assumptions on the stability of parameter estimates and default identification, there are no further requirementson the underlying credit risk parameters . The method is robust irrespective whether parameters are simple, expert based values or highly predictive and perfectly calibrated IRBA compliant methods  .
The dependency structure of credit risk parameters is a key driver for capital consumption and receives regulatory and scientific attention. The impact of parameter imperfections on the quality of expected loss (EL) in the sense of a fair, unbiased estimate of risk expenses however is barely covered. So far there are no established backtesting procedures for EL, quantifying its impact with regards to pricing or risk adjusted profitability measures . In this paper, a practically oriented, top-down approach to assess the quality of EL by backtesting with a properly defined risk measure is introduced. In a first step, the concept of risk expenses (Cost of Risk) has to be extended beyond the classical provisioning view, towards a more adequate capital consumption approach (Impact of Risk, IoR). On this basis, the difference between parameter-based EL and actually reported Impact of Risk is decomposed into its key components . The proposed method will deepen the understanding of practical properties of EL, reconciles the EL with a clearly defined and observable risk measure and provides a link between upcoming IFRS 9 accounting standards for loan loss provisioning with IRBA regulatory capital requirements . The method is robust irrespective whether parameters are simple, expert based values or highly predictive and perfectly calibrated IRBA compliant methods , as long as parameters and default identification procedures are stable .
A
(EL)
D
, such as RAROC or EVA
R
actually observed risk impact on capital
a properly defined risk measure
D
(P
D
L)
D
(PL Backtest and NPL Backtest)
R
aligns
reconciles
R
actually observed risk impact on capital and has the potential to improve the quality of EL-based business decisions. Besides assumptions on the stability of parameter estimates and default identification, there are no further requirementson the underlying credit risk parameters
a clearly defined and observable risk measure and provides a link between upcoming IFRS 9 accounting standards for loan loss provisioning with IRBA regulatory capital requirements
A
, as long as parameters and default identification procedures are stable