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This paper assesses the sensitivity of solvency stress testing results to the choice of credit risk variable and level of data aggregation at which the stress test is conducted. In practice, both choices are often determined by technical considerations, such as data availability. Using data for the Belgian banking system, we find that the impact of a stress test on banks' Tier 1 ratios can differ substantially depending on the credit risk variable and the level of data aggregation considered. If solvency stress tests are going to be used as a supervisory tool or to set regulatory capital requirements, there is a need to further harmonise their execution across institutions and supervisors in order to enhance comparability. This is certainly relevant in the context of the EUwide stress tests, where institutions often use different credit risk variables and levels of data aggregation to estimate the impact of the common methodology and macroeconomic scenario on their capital level while supervisors rely on different models to quality assure and validate banks' results. More generally, there is also a need to improve the availability and quality of the data to be used for stress testing purposes.

This paper decomposes the explained part of the CDS spread changes of 31 listed euro area banks according to various risk drivers. The choice of the credit risk drivers is inspired by the Merton (1974) model. Individual CDS liquidity and other market and business variables are identified to complement the Merton model and are shown to play an important role in explaining credit spread changes. Our decomposition reveals, however, highly changing dynamics in the credit, liquidity, and business cycle and market wide components. This result is important since supervisors and monetary policy makers extract different signals from liquidity based CDS spread changes than from business cycle or credit risk based changes. For the recent financial crisis, we confirm that the steeply rising CDS spreads are due to increased credit risk. However, individual CDS liquidity and market wide liquidity premia played a dominant role. In the period before the start of the crisis, our model and its decomposition suggest that credit risk was not correctly priced, a finding which was correctly observed by e.g. the International Monetary Fund

We address a number of comparative issues relating to the performance of failure prediction models for small, private firms. We use two models provided by vendors, a model developed by the National Bank of Belgium, and the Altman Z-score model to investigate model power, the extent of disagreement between models in the ranking of firms, and the design of internal rating systems. We also examine the potential gains from combining the output of multiple models. We find that the power of all four models in predicting bankruptcies is very good at the one-year horizon, even though not all of the models were developed using bankruptcy data and the models use different statistical methodologies. Disagreements in firm rankings are nevertheless significant across models, and model choice will have an impact on loan pricing and origination decisions. We find that it is possible to realize important gains from combining models with similar power. In addition, we show that it can also be beneficial to combine a weaker model with a stronger one if disagreements across models with respect to failing firms are high enough. Finally, the number of classes in an internal rating system appears to be more important than the distribution of borrowers across classes

This paper analyses the effect of soliciting a rating on the rating outcome of banks. Using a sample of Asian banks rated by Fitch Ratings ("Fitch"), I find evidence that unsolicited ratings tend to be lower than solicited ones, after accounting for differences in observed bank characteristics. This downward bias does not seem to be explained by the fact that betterquality banks selfselect into the solicited group. Rather, unsolicited ratings appear to be lower because they are based on public information. As a result, they tend to be more conservative than solicited ratings, which incorporate both public and nonpublic information.

This paper simulates the minimum capital requirements for the wholesale exposures of a medium-sized bank in each EMU country depending on the credit rating agencies chosen by the bank to risk-weight its exposures in the standardised approach to credit risk in Basel II. Three main results emerge from the analysis. First, although the use of different combinations of credit rating agencies leads to significant differences in minimum capital requirements, these differences never exceed 10% of EMU banks’ regulatory capital for wholesale exposures on average. Second, the standardised approach provides a small regulatory capital incentive for banks to use several credit rating agencies to risk-weight their exposures. Third, the minimum capital requirements for the wholesale exposures of EMU banks will be higher in Basel II than in Basel I. I also show that the incentive for banks to engage in regulatory arbitrage in the standardised approach to credit risk is limited.