The fundamentals of credit

This chapter focuses on methodologies involved in the calculation of stand alone, its standards measures (cumulative default frequency this is simply in recognition of the fact that the chances of default generally increase over time), marginal default probability (the potentially variable default probability for year), default intensity (the time dependent probability of defaulting, in a very small period of time), a portfolio as a set of standalones (the portfolio, unfortunately from the perspective of default, is a very complicated beast. The industry approaches it in a sequence of stages, each stage adds in another layer of reality), and the corelation (the so-called joint default probability approach and the technique of inferring the correlation from observations on the number of defaults within a large portfolio). This chapter discusses several approaches to portfolio management. The two approaches—default probability approach and the technique of inferring the correlation from observations on the number of defaults within a large portfolio—assume a constant default rate or a number of rates, still constant but applicable to different categories. A major approach to portfolio default modeling is a type of mean variance approach under which the default rate is assumed to have some mean and randomness. The basic requirement is that the data on defaults is available as a time series, that is, an average and volatility can be calculated. A method to obtain the default correlation is to infer it from underlying data. The big assumption is that the data is homogenous in the sense that the individual companies that comprise the bulk applies to issuers having the same sector and rating. The assumption is then made that there exists a uniform default rate, which is constant over the life time of the observations and this rate applies to all the obligors.