Neural Networks and Statistical model Sale
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.
You might also like:
Stock market analysts on trial2002-02-09 14:24:25 by on-trial
The amount of poor and self-interested advice that is being issued by brokerages and their analysts. To this day, the majority of stockbrokers are compensated on the number of trades their customers make, not on the returns they generate for them or on the quality of the advice they provide. We believe that the price targets and analyst ratings are made with several masters in mind, none of whom are the individual investor. In a similar fashion, sell-side stock analysts are generally compensated based upon the overall profitability of their firms, not the quality or accuracy of their analysis. In the end, analysts have minimal structural incentive to be accurate in their predictions; rather their built-in incentive is to be as favorable to their corporate clients as possible. It is a...
Gurus' Results Stay Consistently Bad — Forbes
Investment gurus make their money selling market predictions, not following them. Their overall performance has been historically and consistently dismal. Why people pay for market predictions is a one of Wall Street's biggest mysteries.
Neural Networks and Simulation Methods (Electrical and Computer Engineering)
Book (CRC Press)