Using a unique data set on German banks' loans to the German real economy, we investigate banks' credit risk. This data set includes the volume of loans per bank and industry as well as the corresponding write-downs. Our empirical study for the period 2003-2011 yields the following results: (i) Beyond the nationwide credit loss rate, industry composition, and regional factors, the loans' maturity structure is found to drive the bank-wide loss rates in the credit portfolio. (ii) The nationwide loss rate has the most impact, followed by the maturity structure and the industry composition. (iii) For nationwide banks, these common factors explain about 26% of the time variation in the loss rate of credit portfolios; for regional banks, this percentage is less than eight percent.
KeywordsCredit risk, systematic risk, maturity, stress tests
JEL Classification
G21
Non-technical summaryHow much default events depend on systematic factors which have an impact on entire borrower groups plays a key role in the default risk of credit portfolios. The stronger the influence of such factors is, the less useful is a diversification across a large number of borrowers and the stronger are the fluctuations in portfolio losses over time. As a bank has to use capital to absorb loss fluctuations, it is crucial for both risk managers and regulators to identify systematic factors and be aware of their relative influence.A direct probability estimate of common defaults is inappropriate, as it is in the nature of the credit business that defaults -and let alone common defaults -rarely occur.First, asset value models are used in practice, which recognize the loans as a derivative of the (non-observable) firm value of borrowers. The systematic factors of such models are generally not observable. Second, "intensity-based" models are employed. Their systematic factors can be interpreted as an average default rate in a given sector (a branch of industry, e.g.) at a given time. In both types of model, the borrowers have to be assigned to suitable groups, preferably so that the link between the defaults is as large as possible within the group and as small as possible between the groups. Allocation by industrial sector is usual, but neither exhaustive nor obligatory. In principle, other classification criteria can be just as meaningful. This is the point of departure for our study. We use a Bundesbank dataset, which covers all German on-balance-sheet credit business with the real economy from 2003 to 2011. It contains credit volumes and write-downs for every bank, broken down into borrower categories and maturity bands. In addition, many credit exposures can be assigned to a region. Our empirical model is essentially an intensity-based approach, as we calculate systematic factors as averages of individual write-down rates.We show that up to 26 percent of the temporal variation of bank-specific write-down rates can be explained by four systematic components. Besides the nationwide loss rate, the portfolio composition with respect to indu...