Neural Networks book by Simon Haykin
The usual qualitative assessment of individual risks of borrowers, other than individuals, relies on ratings. However, models of individual risks have existed for a long time. Due to the current emphasis on extensive data on individual borrowers risks, modelling became attractive for assessing risk in a comprehensive and objective manner, and for complying with the New Basel Accord recommendations.
Statistical models link observable attributes of borrowers to actual ratings or to observed default or no default events. The alternative view consists of considering that a drop of the asset value of a firm below the threshold of short-term debt obligations triggers the firms default. This second approach, based on the Merton (1974) model, relies on values derived from equity prices of publicly listed firms. Statistical techniques do not rely on conceptual models, as the Merton model does, but on statistical fits. This chapter addresses the statistical approaches. There are several generations of models of credit risk and default probabilities, starting from the early statistical models linking ratings to financial characteristics of firms, up to elaborate econometric techniques and neural network models. This chapter focuses on statistical techniques. The next chapter focuses on the option modelling of default probability, its conceptual foundations and implementation techniques.
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