We analyze the dynamics of banks’ regulatory capital ratios. Using monthly regulatory data of large German banks, we estimate the target level and the adjustment speed of the capital ratio for each bank separately. There exists a target level for a substantial percentage of banks. Unlike with panel regressions, we can estimate individual adjustment speeds and find large variation across banks. Adjustments on the liability side are most effective, although adjustment rates on the asset side are higher. Private commercial banks (neither state-owned nor cooperative) and banks with a high level of proprietary trading are more likely to adjust their capital ratio tightly. Banks with a target capital ratio compensate for low target ratios with low asset volatilities and high adjustment speeds. They seem to care mainly about the resulting probability to comply with the regulatory minimum. Assuming low variation of this probability explains most of the large cross-sectional variation of bank capital.

We examine pitfalls in the use of return-based measures of systemic risk contributions (SRCs). For both linear and nonlinear return frameworks, assuming normal and heavy-tailed distributions, we identify nonexotic cases in which a change in a bank’s systematic risk, idiosyncratic risk, size, or contagiousness increases the risk of the system but lowers the measured SRC of the bank. Assessments based on estimated SRCs could thus produce false interpretations and incentives. We also identify potentially adverse side effects: A change in a bank’s risk structure can make the measured SRC of its competitors increase more strongly than its own.

Using a unique data set on German banks’ loans to the German real economy, we investigate banks’ credit risk. This data set contains the volume of loans, and write-downs on loans, per bank and industry. Our empirical study for the period 2003–2011 yields the following results: (i) alongside the average nationwide credit loss rate, industry composition, regional factors, and the state of the global economy, the loans’ maturity structure is identified as an additional driver of the bank-wide loss rates in the credit portfolio. (ii) The nationwide loss rate has the largest 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 figure is less than 8%.