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RISIKO MANAGER 10.2019

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RISIKO MANAGER ist das führende Medium für alle Experten des Financial Risk Managements in Banken, Sparkassen und Versicherungen. Mit Themen aus den Bereichen Kreditrisiko, Marktrisiko, OpRisk, ERM und Regulierung vermittelt RISIKO MANAGER seinen Lesern hochkarätige Einschätzungen und umfassendes Wissen für fortschrittliches Risikomanagement.

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14 RISIKO MANAGER 10|2019 » the related risk characteristics, including the type of product and type of collateral; » location of the collateral, unfunded credit protection, seniority, Loan-to-Value exposure size, seasoning, and recovery procedures (for protection please consider the Handbook on European data protection law 2012 by the Council of Europe); » transaction related risk characteristics, including the type of product, type of collateral, LTV loan to value ratio, recovery procedures, etc. » obligor related risk characteristics and where applicable, size, capital structure, region, industrial sector, and line of business; » bank related factors, including internal organization and internal governance, events such as mergers, and the existence of specific entities within the group recoveries such as 'bad credit banks'; » external factors, including interest rates, legal framework and other factors influencing the length of the recovery process. Banks consider the risk drivers not only at the moment of default but also at least fore default. Realized LGD may be zero when recoveries offset the direct or indirect costs associated with collecting on the default and the effects of discounting. Particular attention should be given to zero-loss exposures, since they may reveal some issues with the calculation of realized losses. Where LGD estimates for specific facility grades or pools are low, banks should demonstrate the accuracy of their estimation process. Observed average LGD Banks are required to calculate the observed average LGD for each facility grade or pool. The level of portfolio covered by the LGD model takes into account realized LGDs on all defaults observed in the historical observation period related to closed recovery processes without including any expected future recoveries. The observed average LGD is weighted by the number of defaults included in the calculation. Banks define the maximum period of the recovery process for a given type of exposure from the moment of default that reflects the expected period time observed on the closed recovery processes during which the bank realizes the most of the recoveries. The bank should not consider the outlier observations with significantly longer recovery processes. The length of the maximum period of the recovery processes may be different for different types of exposures. This specification of the maximum period of the recovery process are documented and supported by evidence of the observed recovery patterns, and should be coherent with the nature of the transactions and the type of exposures. All exposures that remain in defaulted status for longer than the maximum period of the recovery process specified for this type of exposures, are treated as a closed recovery process for calculation of the observed average LGD, considering only the recoveries realized. Banks obtain the long-run average LGD by adjusting the observed average LGD (considering information related to incomplete recovery processes and the estimated future costs and recoveries on these exposures) with the specific conditions. If the time from the moment of default until the moment of estimation is longer than the maximum period of the recovery, the process specified for this type of exposures banks: i. take into account all observed recoveries realized before or after the maximum period of the recovery process i. shall not estimate any future recoveries. If the time from the moment of default until the moment of estimation is shorter than the maximum period of the recovery process specified for this type of exposure, future recoveries are based on those stemming from the realization of the existing collaterals, and those to be realized without the use of collaterals. The maximum period of the recovery processes should be specified in such a way that ensures sufficient data for the estimation of the recoveries within the period for the incomplete recovery processes. The length of the maximum period of the recovery process may be different for different types of exposures. All exposures that remain in defaulted status for the maximum period of the recovery process specified for this type of exposure are treated as closed recovery process for calculation LGD, considering only the recoveries realized so far. Banks define the maximum period of the recovery process for a given type of exposure from the moment of default that reflects the expected period time observed on the closed recovery processes, during which the bank realizes the most of the recoveries without taking into account the outlier observations with significantly longer recovery processes. The maximum period of the recovery processes is specified in such a way that ensures sufficient data for the estimation of the recoveries within this period for the incomplete recovery processes. Where direct LGD estimates are used for the calculation of capital requirements following article 169(3) of the CRR, Banks are expected to calculate the long-run average LGD at a level (other than facility grade or pool) appropriate for the application of the model. This can be found when the continuous LGD is the result of a combination of different components (e.g. secured and unsecured components). Here, the long-run average expectations are applied at the level of each of the components. Banks ensure that no bias is introduced in the estimation when combining the different components. Banks also consider whether the value of repossession adequately reflects the value of the repossessed collateral, consistently with any established internal requirements for collateral management, legal certainty and risk management. If there is significant uncertainty whether the value of repossession adequately reflects the value of the repossessed collateral, banks should apply an appropriate haircut to this value and include in the calculation of economic loss recovery as a value of repossession after the haircut. Banks should estimate this haircut taking into account all of the following conditions: a) the haircut should reflect the possible errors in the valuation of the collateral at the moment of repossession, taking into account the type of the valuation available at the moment of repossession, the date it was performed and the liquidity of the market for this type of asset b) the haircut should be estimated with the assumption that the bank intends to sell

Kreditrisiko 15 the repossessed collateral to an independent third party, and should reflect the potential price that could be achieved from such a sale, the costs of the sale and the discounting effect to the moment of repossession, taking into account the liquidity of the market for this type of assets; c) when there are observations available regarding the repossessions and subsequent sales of similar types of collaterals, the estimation of the haircut should be based on these observations and regularly back-tested. Tests of LGD Completeness check Missing value analysis is performed by computing the proportion of facilities without identifiers (blank values or default values) or erroneous identifiers. All databases used in the IRB workflow from source databases through to historical LGD data should be investigated. The proportion of missing values for each database/dataset identified in the scope in terms of number and exposure, and the information relating to the databases/datasets used, must be evaluated. Uniqueness test Duplicates should be counted due to facility identifiers across the IRB workflow. The facility identification primary key has to be inspected from source databases through to historical LGD data. Duplicates are counted for each database/dataset identified in the scope in terms of number and exposure. Information relating to the databases/datasets has been published. (Cross) consistency check Reconciliation between historical LGD data and historical PD data when possible is validated, verifying that the same facility identifiers are used for the same facilities through historical LGD data and historical PD data. The difference in terms of number and exposure between the reconciled databases should be, and an explanation of the differences in information relating to the used databases/datasets must be reported. LGD Testing The following table shows examples of additional validity checks for LGD drives. Downturn LGD Banks are expected to compare their estimates with a reference value. An economic downturn in terms of economic and credit indicators, based on economic and credit indicators over a historical period is determined. The length of the historical dataset of economic indicators should be derived from the past 20 years to date. At a minimum, banks should consider for all exposure types indicators (interpreted separately) such as GDP growth, unemployment rates, interest rates, inflation rates, system-wide default rates, and credit losses, complemented with internal series (i.e. default rates, losses). Additional indicators should be considered for the following types of exposure: i. exposures to "corporate and retail SME" – sectoral/industry indexes; ii. exposures to "residential real estate" – house prices, region-specific indexes; iii. exposures to "other retail" – consumer leverage ratio39 or similar information. The specified downturn period should be a minimum of one year, although longer periods are acceptable when historical data shows longer stress periods for some indicators, or peaks or troughs of different Tab. 04 Checks for validity of LGD Driver Variable Type Plausible outcomes Validity tests Scope LTV Percentage/ Ratio ]0, 1.5] 1) Trend analysis: distribution comparison of the LTVs in the LGD calibration database over the last three years (graphically or using statistics describing the central tendency, the dispersion, the asymmetry and the tailedness of the variables) 2) Compute the % of obligors /facilities with a LTV outside the interval (0, 1.5) 1) the LGD historical data(set) 2) each database from the source up to the LGD historical dataset Collateral Type Nominal Internal nomenclature 1) Trend analysis: distribution comparison of the mortgage type over the last three years (graphically) The LGD historical dataset Collateral Value Amount ]0, 2x loan value] 1) Trend analysis: distribution comparison of the collateral values over the last three years (graphically or using statistics describing the central tendency, the dispersion, the asymmetry and the "tailedness" of the variable 2) Compute the % of obligors/facilities with a ratio “Collateral value“/“Loan Value“ outside the interval 1) The LGD historical dataset 2) Each database from the source up to the LGD historical dataset Vintage for mortgages Date Vintage < current date Compare the number of mortgages in each vintage a explain the tendency (economic reasons, business strategy ...) Each database from the source up to the LGD historical dataset

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