Company: ADZCF
Filing Date: 2025-03-13
Form Type: 20-F
Source: 0001159508-25-000020
Chunk: 259

Company: DEUTSCHE BANK AKTIENGESELLSCHAFT
Filing Date: 2025-03-13
Form: 20-F
Chunk 259
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 available quantitative and qualitative information. Expert-based models are usually applied for borrowers in the exposure classes “Central governments and central banks”, “Institutions” and “Corporates” with the exception of those “Corporates” for which a sufficient data basis is available for statistical scoring models. For the latter, as well as for the retail segment, statistical scoring or hybrid models combining both approaches are commonly used. Quantitative rating methodologies are developed based on applicable statistical modelling techniques, such as logistic regression.

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| Deutsche Bank      |
| Annual Report 2024 |

One-year PDs are extended to multi-year PD curves using through-the-cycle matrices and macroeconomic forecasts. Based on economic scenarios centered around the macroeconomic baseline forecast, through-the-cycle matrices are first transformed into point-in-time rating migration matrices, typically for a two-year period. The calculation of the point-in-time matrices provides the link between macroeconomic variables and the default and rating behavior of borrowers, which is derived from time series through regression techniques involving macroeconomic variables (MEVs) and historical rating and default data. In a final step, multi-year PD curves are derived from point-in-time rating migration matrices for periods where reasonable and supportable forecasts are available and extrapolated based on through-the-cycle rating migration matrices beyond those periods. The second key input factor into the ECL calculation is the LGD parameter, which is defined as the likely loss intensity in case of a borrower’s default. It provides an estimation of the exposure that cannot be recovered in a default event and therefore captures the severity of a loss. Conceptually, LGD estimates are independent of a borrower’s probability of default. The LGD models applied in Stages 1 and 2, which are based on regulatory LGD models, but adjusted for IFRS 9 requirements (i.e., removal of downturn-add-on and removal of indirect costs of workout), ensure that the main drivers for losses (i.e., different levels and quality of collateralization and customer or product types or seniority of facility) are reflected as risk drivers in LGD estimates. In the bank’s LGD models, the Group assigns collateral type specific LGD parameters to the collateralized exposure (collateral value after application of haircuts). Forward-looking information (FLI) is also incorporated into LGD projections in terms of FLI LGD scaling factors for exposures in Stages 1 and 2 based on forecasts for key MEVs. LGD adjustments are quantified relative to long-term historical averages, which represent a neutral