Measuring business sector concentration by an infection model
AbstractResults from portfolio models for credit risk tell us that loan concentration in certain industry sectors can substantially increase the value-at-risk (VaR). The purpose of this paper is to analyze whether a tractable "infection model" can provide a meaningful estimate of the impact of concentration risk on the VaR. I apply rather parsimonious data requirements, which are comparable to those for Moody's Binomial Expansion Technique (BET) and considerably lower than for a multi-factor model. The infection model extends the BET model by introducing default infection into the hypothetical portfolio on which the real portfolio is mapped in order to obtain a simple solution for the VaR. The infection probability is calibrated for a range of typical values of input parameters, which capture the concentration of a portfolio in industry sectors, default dependencies between exposures and their credit quality. The accuracy of the new model is measured for test portfolios with a realistic industry-sector composition, obtained from the German central credit register. I find that a carefully calibrated infection model provides a reasonably close approximation to the VaR obtained from a multi-factor model and outperforms by far the BET model. The simulation results suggest that the calibrated infection model promises to provide a fit-for-purpose tool to measure concentration risk in business sectors that could be useful for risk managers and banking supervisors alike. --
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Bibliographic InfoPaper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 2: Banking and Financial Studies with number 2006,03.
Date of creation: 2005
Date of revision:
asset correlation; concentration risk; credit risk; multi-factor model; value-at-risk;
Find related papers by JEL classification:
- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-08-05 (All new papers)
- NEP-BAN-2006-08-05 (Banking)
- NEP-FIN-2006-08-05 (Finance)
- NEP-FMK-2006-08-05 (Financial Markets)
- NEP-RMG-2006-08-05 (Risk Management)
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