Pitfalls in modeling loss given default of bank loans

The parameter loss given default (LGD) of loans plays a crucial role for risk-based decision making of banks including risk-adjusted pricing. Depending on the quality of the estimation of LGDs, banks can gain significant competitive advantage. For bank loans, the estimation is usually based on discounted recovery cash flows, leading to workout LGDs. In this paper, we reveal several problems that may occur when modeling workout LGDs, leading to LGD estimates which are biased or have low explanatory power. Based on a data set of 71,463 defaulted bank loans, we analyze these issues and derive recommendations for action in order to avoid these problems. Due to the restricted observation period of recovery cash flows the problem of length-biased sampling occurs, where long workout processes are underrepresented in the sample, leading to an underestimation of LGDs. Write-offs and recoveries are often driven by different influencing factors, which is ignored by the empirical literature on LGD modeling. We propose a two-step approach for modeling LGDs of non-defaulted loans which accounts for these differences leading to an improved explanatory power. For LGDs of defaulted loans, the type of default and the length of the default period have high explanatory power, but estimates relying on these variables can lead to a significant underestimation of LGDs. We propose a model for defaulted loans which makes use of these influence factors and leads to consistent LGD estimates. --Credit risk,Bank loans,Loss given default,Forecasting

Concentration risk under Pillar 2: When are credit portfolios infinitely fine grained?

The ongoing debate concerning credit concentration risk is mainly driven by the requirements on credit risk management due to Pillar 2 of Basel II since risks (e.g. concentration risk) that are not fully captured by Pillar 1 should be adequately considered in the banks' risk management. This instruction is indeed relevant since quantifying credit portfolio risk in Pillar 1 is based on an Asymptotic Single Risk Factor (ASRF) framework in which concentration risk is not covered. Against the background of the ASRF model, we determine the number of credits up to which concentration risk leads to a significant estimation error so that the assumption of an infinitely fine grained portfolio is inadequate. We conclude that the critical portfolio size varies from 22 up to 35,986 debtors, dependent on assets correlation and probability of default. Using a modified valuation function (granularity adjustment) it is possible to reduce the critical number of credits by averaged 83.04 %. --Basel II,Pillar 2,Concentration Risk,Granularity Adjustment

Measuring concentration risk for regulatory purposes

The measurement of concentration risk in credit portfolios is necessary for the determination of regulatory capital under Pillar 2 of Basel II as well as for managing portfolios and allocating economic capital. Existing multi-factor models that deal with concentration risk are often inconsistent with the Pillar 1 capital requirements. Therefore, we adjust these models to achieve Basel II-compliant results. Within a simulation study we test the impact of sector concentrations on several portfolios and contrast the accuracy of the different models. In this context, we also compare Value at Risk and Expected Shortfall regarding their suitability to assess concentration risk. --Concentration Risk,Pillar 2,Multi-Factor Models,Economic Capital,Simulation Study,Value at Risk,Expected Shortfall

Einsatz inflationsindexierter Anleihen im Asset-Liability-Management

Es wird dargelegt, dass das im Rahmen des Asset-Liability-Managements häufig gewählte Immunisierungsverfahren des Durationsmatch unter Verwendung der traditionellen Yieldbeta-Methode nur dann sachgerecht eingesetzt werden kann, wenn das betrachtete Unternehmen keinen sicheren realen und damit der Inflationsunsicherheit unterliegenden nominalen Zahlungsverpflichtungen gegenübersteht. Vor diesem Hintergrund wird die Yield-beta-Methode erweitert, um das Verfahren auch auf sichere reale Zahlungsverpflichtungen anwenden zu können, die häufig bei Versicherungen anzutreffen sind. Für die Umsetzung benötigt man zusätzlich Anlageinstrumente, die sichere reale Einzahlungsüberschüsse verbriefen. Für den vorliegenden Beitrag werden inflationsindexierte Anleihen gewählt, die jüngst auch von der Bundesrepublik Deutschland emittiert wurden. Verschiedene Typen inflationsindexierter Anleihen werden vorgestellt, und ihr Einsatz im Asset-Liability-Management wird dargelegt

Concentration risk under Pillar 2: When are credit portfolios infinitely fine grained?

The ongoing debate concerning credit concentration risk is mainly driven by the requirements on credit risk management due to Pillar 2 of Basel II since risks (e.g. concentration risk) that are not fully captured by Pillar 1 should be adequately considered in the banks' risk management. This instruction is indeed relevant since quantifying credit portfolio risk in Pillar 1 is based on an Asymptotic Single Risk Factor (ASRF) framework in which concentration risk is not covered. Against the background of the ASRF model, we determine the number of credits up to which concentration risk leads to a significant estimation error so that the assumption of an infinitely fine grained portfolio is inadequate. We conclude that the critical portfolio size varies from 22 up to 35,986 debtors, dependent on assets correlation and probability of default. Using a modified valuation function (granularity adjustment) it is possible to reduce the critical number of credits by averaged 83.04 %

Markowitz versus Michaud: Portfolio optimization strategies reconsidered

"Several attempts have been made to reduce the impact of estimation errors on the optimal portfolio composition. On the one hand, improved estimators of the necessary moments have been developed and on the other hand, heuristic methods have been generated to enhance the portfolio performance, for instance the 'resampled efficiency' of Michaud (1998). We compare the out-ofsample performance of traditional Mean-Variance optimization by Markowitz (1952) with Michaud's resampled efficiency in a comprehensive simulation study for a large number of relevant estimators appearing in the literature. In this context we consider different estimation periods as well as unconstrained and constrained portfolio optimization problems. The main finding of our simu-lation study concerning the optimization approach is that Markowitz outperforms Mi-chaud on average. Furthermore, the estimation strategy of Frost/Savarino (1988) proves to work excellent in all analyzed situations." (author's abstract

Measuring concentration risk for regulatory purposes

The measurement of concentration risk in credit portfolios is necessary for the determination of regulatory capital under Pillar 2 of Basel II as well as for managing portfolios and allocating economic capital. Existing multi-factor models that deal with concentration risk are often inconsistent with the Pillar 1 capital requirements. Therefore, we adjust these models to achieve Basel II-compliant results. Within a simulation study we test the impact of sector concentrations on several portfolios and contrast the accuracy of the different models. In this context, we also compare Value at Risk and Expected Shortfall regarding their suitability to assess concentration risk

CUSTOMER-ORIENTED CONFIGURATION SYSTEMS: ONE TYPE FITS ALL?

Product configuration systems are useful instruments of individualization in the field of mass customization. Recent studies have shown that the two factors Expertise and the Motivation to process information have a significant influence on the preference of configurators. In this paper we consider the influence of those two factors on the fit of two types of configuration systems: Parameter-based and Needs-based configurators. While a Parameter-based system allows users to specify design parameters, a Needs-based configurator calculates those parameters based on the users weighted needs. To test the fit of each configurator depending on the Expertise and the Motivation of users, we carried out an experiment. Therefore, we developed a prototype for both types of configuration systems. We found out that Parameter-based systems are more appropriate for customers with high Expertise and high Motivation to process information. Contrary, for customers with low Expertise and Motivation companies are better advised to use Needs-based configurators

Einsatz inflationsindexierter Anleihen im Asset-Liability-Management

Es wird dargelegt, dass das im Rahmen des Asset-Liability-Managements häufig gewählte Immunisierungsverfahren des Durationsmatch unter Verwendung der traditionellen Yieldbeta-Methode nur dann sachgerecht eingesetzt werden kann, wenn das betrachtete Unternehmen keinen sicheren realen und damit der Inflationsunsicherheit unterliegenden nominalen Zahlungsverpflichtungen gegenübersteht. Vor diesem Hintergrund wird die Yield-beta-Methode erweitert, um das Verfahren auch auf sichere reale Zahlungsverpflichtungen anwenden zu können, die häufig bei Versicherungen anzutreffen sind. Für die Umsetzung benötigt man zusätzlich Anlageinstrumente, die sichere reale Einzahlungsüberschüsse verbriefen. Für den vorliegenden Beitrag werden inflationsindexierte Anleihen gewählt, die jüngst auch von der Bundesrepublik Deutschland emittiert wurden. Verschiedene Typen inflationsindexierter Anleihen werden vorgestellt, und ihr Einsatz im Asset-Liability-Management wird dargelegt. --Asset-Liability-Management,inflationsindexierte Anleihen,Durationsmatch,BPV-Match,Yield-beta-Methode