Increasing international competition and changes in the regulatory framework driven by the Basel Committee on Banking Supervision (Basel II) called forth incentives for banks to improve their credit rating systems. In a competitive framework a poor statistical power of a bank's internal rating system will deteriorate the economic performance due to adverse selection, i. e. customers with a better credit quality than assessed by the bank will potentially walk away and leave the bank with a portfolio of customers with a credit quality lower than estimated. Obviously, improving the statistical power of a rating system will have a positive impact on economic performance.
The size of this effect depends mainly on the degree of competitivity of the market environment. The counterweight of these potential benefits are the costs of investing into the power of a rating system such as organizational costs, costs of information technology, and costs of collecting and managing the required data. In addition, a bank's internal rating system with sufficient statistical power might be used for calculating the regulatory capital requirements set by the Basel II Internal Ratings Based Approaches which are expected to be lower than in the Modified Standardized Approach. In addition it can be shown that due to the concave relation between regulatory capital requirements and default probabilities even for banks having already qualified for the Internal Ratings Based Approach a more accurate rating system which enables a finer grained rating class structure leads to lower capital requirements.
It is the main objective of this paper to model the decision whether to invest into the quality of a rating system in a rather general framework. Our model is aimed to quantify the benefits of such an investment. The first part of our analysis is focused on the economic value of increasing the statistical power of a bank's internal rating system. In line with the work by Jordão and Stein (2003) we compare the profitability of prototypical banks with different statistical power of their rating systems in different market environments.
In our model the statistical power of a rating system depends on several parameters such as its accuracy and the rating class structure. We measure the accuracy of forecasting individual default probabilities as the variance of the deviations of the forecasted from the actual default probabilities. In this setup this measure is more closely related to the economic impact than the area-under-the-curve measures traditionally used by other researchers.
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Modelling the Economic Value of Credit Rating Systems
