Sector Concentration in Loan Portfolios and Economic Capital

The purpose of this paper is to measure the potential impact of business-sector concentration on economic capital for loan portfolios and to explore a tractable model for its measurement. The empirical part evaluates the increase in economic capital in a multi-factor asset value model for portfolios with increasing sector concentration. The sector composition is based on credit information from the German central credit register. Finding that business sector concentration can substantially increase economic capital, the theoretical part of the paper explores whether this risk can be measured by a tractable model that avoids Monte Carlo simulations. We analyze a simplified version of the analytic value-at-risk approximation developed by Pykhtin (2004), which only requires risk parameters on a sector level. Sensitivity analyses with various input parameters show that the analytic approximation formulae perform well in approximating economic capital for portfolios which are homogeneous on a sector level in terms of PD and exposure size. Furthermore, we explore the robustness of our results for portfolios which are heterogeneous in terms of these two characteristics. We find that low granularity ceteris paribus causes the analytic approximation formulae to underestimate economic capital, whereas heterogeneity in individual PDs causes overestimation. Indicative results imply that in typical credit portfolios, PD heterogeneity will at least compensate for the granularity effect. This suggests that the analytic approximations estimate economic capital reasonably well and/or err on the conservative side.sector concentration risk, economic capital

Sector concentration in loan portfolios and economic capital

The purpose of this paper is to measure the potential impact of business-sector concentration on economic capital for loan portfolios and to explore a tractable model for its measurement. The empirical part evaluates the increase in economic capital in a multi-factor asset value model for portfolios with increasing sector concentration. The sector composition is based on credit information from the German central credit register. Finding that business sector concentration can substantially increase economic capital, the theoretical part of the paper explores whether this risk can be measured by a tractable model that avoids Monte Carlo simulations. We analyze a simplified version of the analytic value-at-risk approximation developed by Pykhtin (2004), which only requires risk parameters on a sector level. Sensitivity analyses with various input parameters show that the analytic approximation formulae perform well in approximating economic capital for portfolios which are homogeneous on a sector level in terms of PD and exposure size. Furthermore, we explore the robustness of our results for portfolios which are heterogeneous in terms of these two characteristics. We find that low granularity ceteris paribus causes the analytic approximation formulae to underestimate economic capital, whereas heterogeneity in individual PDs causes overestimation. Indicative results imply that in typical credit portfolios, PD heterogeneity will at least compensate for the granularity effect. This suggests that the analytic approximations estimate economic capital reasonably well and/or err on the conservative side. --sector concentration risk,economic capital

Asset correlations and credit portfolio risk: an empirical analysis

In credit risk modelling, the correlation of unobservable asset returns is a crucial component for the measurement of portfolio risk. In this paper, we estimate asset correlations from monthly time series of Moody's KMV asset values for around 2,000 European firms from 1996 to 2004. We compare correlation and value-atrisk (VaR) estimates in a one-factor or market model and a multi-factor or sector model. Our main finding is a complex interaction of credit risk correlations and default probabilities affecting total credit portfolio risk. Differentiation between industry sectors when using the sector model instead of the market model has only a secondary effect on credit portfolio risk, at least for the underlying credit portfolio. Averaging firm-dependent asset correlations on a sector level can, however, cause a substantial underestimation of the VaR in a portfolio with heterogeneous borrower size. This result holds for the market as well as the sector model. Furthermore, the VaR of the IRB model is more stable over time than the VaR of the market model and the sector model, while its distance from the other two models fluctuates over time. --Asset correlations,sector concentration,credit portfolio risk

Do specialization benefits outweigh concentration risks in credit portfolios of German banks?

Lending specialization on certain industry sectors can have opposing effects on monitoring (including screening) abilities and on the sectoral concentration risk of a credit portfolio. In this paper, we examine in the first part if monitoring abilities of German cooperative banks and savings banks increase with their specialization on certain industry sectors. We observe that sectoral specialization generally entails better monitoring quality, particularly in the case of the cooperative banks. In the second part we measure the overall effect of better monitoring and the associated higher sectoral credit concentrations on the credit risk of the portfolio. Our empirical results suggest that specialization benefits overcompensate the impact of higher credit concentrations in the case of the cooperative banks. For savings banks, the results on the net effect depend on how specialization is measured. If specialization is gauged by Hirschman Herfindahl indices, the net effect is an increase of portfolio risk due to the higher sectoral concentration. If specialization is instead measured by distance measures, portfolio risk decreases as the impact of better monitoring abilities prevails. --bank lending,loan portfolio,diversification,expected loss,savings banks,cooperative banks,concentration,economic capital,credit risk

Systemic risk contributions: a credit portfolio approach

We put forward a Merton-type multi-factor portfolio model for assessing banks' contributions to systemic risk. This model accounts for the major drivers of banks' systemic relevance: size, default risk and correlation of banks' assets as a proxy for interconnectedness. We measure systemic risk in terms of the portfolio expected shortfall (ES). Banks' (marginal) risk contributions are calculated based on partial derivatives of the ES in order to ensure a full risk allocation among institutions. We compare the performance of an importance sampling algorithm with a fast analytical approximation of the ES and the marginal risk contributions. Furthermore, we show empirically for a portfolio of large international banks how our approach could be implemented to compute bank-specific capital surcharges for systemic risk or stabilisation fees. We find that size alone is not a reliable proxy for the systemic importance of a bank in this framework. In order to smooth cyclical fluctuations of the risk measure, we explore a time-varying confidence level of the ES. --systemic risk contributions,systemic capital charge,expected shortfall,importance sampling,granularity adjustment

An Upgrade for the 1.4 MeV/u Gas Stripper at the GSI UNILAC

The GSI UNILAC will serve as part of an injector system for the future FAIR facility, currently under construction in Darmstadt, Germany. For this, it has to deliver short-pulsed, high-current, heavy-ion beams with highest beam quality. An upgrade for the 1.4 MeV/u gas stripper is ongoing to increase the yield of uranium ions in the desired charge state. The new setup features a pulsed gas injection synchronized with the beam pulse transit to increase the effective density of the stripper target while keeping the gas load for the differential pumping system low. Systematic measurements of charge state distributions and energy-loss were conducted with 238U-ion beams and different stripper gases, including H2 and He. By using H2 as a stripper gas, the yield into the most populated charge state was increased by over 50%, compared to the current stripper. Furthermore, the high gas density, enabled by the pulsed injection, results in increased mean charge states

A Pulsed Gas Stripper for Stripping of High-Intensity, Heavy-Ion Beams at 1.4 MeV/u at the GSI UNILAC

The GSI UNILAC in combination with SIS18 will serve as a high-current, heavy-ion injector for the future FAIR. It has to meet high demands in terms of beam brilliance at a low duty factor (100 mus beam pulse length, 2.7 Hz repetition rate). An advanced 1.4 MeV/u gas stripper setup has been developed, aiming at an enhanced yield into the required charge states. The setup delivers short, high-density gas pulses in synchronization with the beam pulse. This provides an increased gas density at a reduced gas load for the differential pumping system. In recent measurements, high-intensity, heavy-ion beams of U⁴⁺ were successfully stripped and separated for the desired charge state. The modified stripper setup, as well as major results, are presented, including a comparison to the present gas stripper based on a N₂ gas-jet. The stripping efficiency into the desired 28⁺ charge state was significantly increased by up to 60 % using a hydrogen stripper target while the beam quality remained similar