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

Object oriented data analysis in ALEPH

This article describes the status of the ALPHA^{++} project of the ALEPH collaboration. The ALEPH data have been converted from Fortran data structures (BOS banks) into C^{++} objects and stored in a object oriented database (Objectivity/DB), using tools provided by the RD45 collaboration and the LHC^{++} software project at CERN. A description of the database setup and of a preliminary version of an object oriented analysis program is given.This article describes the status of the ALPHA^{++} project of the ALEPH collaboration. The ALEPH data have been converted from Fortran data structures (BOS banks) into C^{++} objects and stored in a object oriented database (Objectivity/DB), using tools provided by the RD45 collaboration and the LHC^{++} software project at CERN. A description of the database setup and of a preliminary version of an object oriented analysis program is given