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Moody s observes that the input nancial ratios are highly nonnormally distributed and consequently adds another layer to the probit regression by introducing transformation functions derived empirically on the nancial ratios. The dependence of ve-year cumulative default probabilities was obtained in a univariate nonparametric analysis. ( Nonparametric estimation refers to a collection of techniques for tting a curve when there is little a priori knowledge about its shape. Many nonparametric procedures are based on using the ranks of numbers instead of the numbers themselves.) This process determines a transformation function T for each ratio xi. These transformation functions were obtained from Moody s proprietary private rm defaults database. Thus, the full probit model estimated in RiskCalc is Prob(Default) = N[ T (x)] where is the row vector of 10 weights to be estimated, T(x) is the column vector of the 10 transformed nancial ratios, and N[ . . . ] is the cumulative standard normal distribution function. Private Firm Model Moody s KMV While this discussion is appropriately located under the heading of the models that rely on nancial statement data, it may be easier to understand this model if you rst read the description of the Moody s KMV public rm model (i.e., Credit Monitor5 and CreditEdge) in the next section of this chapter. The public rm model was developed rst; and the Private Firm Model was constructed with the same logic. The approach of the Private Firm Model is based on dissecting market

That is, the decomposition allows us to remove the restriction to individual configurations and thus to enlarge the samples that can be evaluated Finally the supply of individual components is computed from these estimates In order to actually build the graphical model, a relational network is constructed first based on a catalog of technical and marketing rules that describes the constructible cars, that is, that distinguishes the possible from the impossible configurations For example, for the VW Golf this catalog, which is formulated in a kind of a logical calculus, comprises about 10 000 entries The idea of starting from this catalog is that impossible configurations should be clearly represented as impossible, because the numbers predicted for such configurations must necessarily be zero If impossible configurations were not explicitly marked as impossible, this requirement could not be enforced, thus making the subsequent prediction worse than necessary.

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tmp = numpy.zeros(n) for j in range(n): s = 0.0 if w[j] <> 0: for i in range(m): s += u[i, j]*b[i] s /= w[j] tmp[j] = s x = numpy.zeros(n)

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The construction of the relational model is carried out in three steps In the first step the catalog of technical and marketing rules is translated into a relational representation That is, all rules that refer to the same set of attributes (that is, the same set of property families) is represented by a relation over this set of attributes: starting from the Cartesian product of the attribute domains all tuples are discarded that are incompatible with one of the rules In a second step these relations are organized in a lattice structure defined by the subset relation on the domains of the relations (that is, the sets of attributes underlying them) The maximal elements of the resulting lattice are used as the maximal cliques of the graph of a relational graphical model In order to make inferences possible this graph is finally triangulated.

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