13
Graphical Data Representation in Bankruptcy Analysis

Variable No.
Fig. 9. Accuracy ratios for bivariate SVM models. Each model includes variable K5
and one of the remaining. Box-plots are estimated basing on 100 random subsamples.
Here a k-nearest-neighbour Gaussian kernel was used. h is the kernel band-
width.
The preliminary PDs evaluated in this way are not necessarily a mono-
tonical function of the score. The monotonisation of PDi, i = 1, 2, . . . , n is
achieved at step two using the Pool Adjacent Violator (PAV) algorithm ([2]
and [13]). As a result we obtain monotonised probabilities of default P D(xi)
for the observations of the training set.
Finally, at step three the PDs are computed for any observation described
with x as an interpolation between two PDs of the neighbouring, in terms of
the score, observations from the training set, xi and xi-1 , i = 2, 3, . . . , n:
PD(x) = PD(xi) + f (x) - f(xi-1) {PD(xi) - PD(xi-ι)} . (12)
f(xi) - f(xi-1)
If the score for an observation x lies beyond the range of scores for the training
set, then P D(x) equals to the score of the first neighbouring obseration of the
training set.
Figure 12 is an example of the cumulative PD curve (power curve) and
estimated PDs for a subsample of 200 companies. The PD curve has a plateau
area for the observations with a high score. Default probabilities can change
from 15% to 80% depending on the score.
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