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Chapter 5
An Alternative Approach
The results from our validity test suggest that the current approach of the implied Gini is not valid. Also, due to structure of the approach, the implied Gini is difficult to modify such that it can be implemented as a valid approach. Hence, we need to develop an approach that is able with the current available data to support the assessment of an LGD model’s potential discriminatory power.
For the development of an alternative approach we have to make choices due to the avail- ability of data and cope with the current data quality. For the development of the alternative approach we limit ourselves by using the AR as a summary statistic. The main reasons for this choice are that for other summary statistics similar questions remain (e.g. ”what are acceptable values?”), and that the usage of the AR is common practice at the bank (and industry).
Within the process of developing an alternative approach we have to cope with several is- sues that cannot be solved due to the lack of data availability. Therefore, we have to make choices, which can be considered to be non-optimal. As we have described in Chapter 3 the implied Gini developed by the bank only uses LGD estimates to determine a models potential discriminatory power. Therefore, we only have these LGDs available with the cor- responding realizations, the LRs. We acknowledge that in principle an LGD is an expected value of a probability distribution based on various drivers or variables of a debtor. Unlike PD models, we do not know the exact relationship between an estimated LGD and a realized LR. In the case of PD models we know that this relationship is always binomial. For LGDs frequently the assumption is made that this relationship is beta on a portfolio level, but from our empirical data we concluded that this does not always hold true for each portfolio. If we would like to assess this relationship more in-depth, we would need additional informa- tion concerning the LGDs (aside from the fact that the number of debtors in some cases is limited). The individual variables or drivers that led to an estimated LGD for an instance are, however, difficult to obtain or not available anymore. If the original drivers were available, we could have chosen to create within a portfolio different classes of debtors, which all have their own specific characteristics. Then we would be able to determine a specific probability distribution for the LRs of a class of debtor. The current situation concerning our data sets is that everything is aggregated within one portfolio (based on product class or type e.g. mortgages) without knowing the individual drivers behind the LGDs. Therefore, we cannot truly assess in an ideal situation world situation what a ’perfect’ model estimation of an LR would be. Given our situation we can only state that in the case of a ’perfect’ model you would like to have estimated the loss perfectly, hence the LGD equals the LR. Strictly taken
CHAPTER 5. AN ALTERNATIVE APPROACH 49
this is of course not ideal. We argue, however, that regardless of this limitation, we can still proof that the current review approach of the bank can be improved by taking into account different portfolio characteristics. The alternative approach for the implied Gini we developed in this research still indicates that setting the same fixed threshold value for each portfolio is unnecessary penalizing particular portfolios. Our approach gives more insight in realistic AR values for a specific portfolio compared to the current situation. We discuss our developed approach in this chapter and provide additional insights in explanatory factors that influence the performance of an LGD model. This chapter has the following outline:
Section 5.1: We describe our proposed alternative approach, which we have developed in order to cope with the current data availability. We explain various choices we have made and why our approach improves the current situation. Fur- thermore, we discuss the trade-off between the practicality of the approach and the ’true’ perfect model.
Section 5.2: We discuss the mathematical formulation of our approach and how we can simulate animplied AR for a specific model. We only do this for LGD mod- els, which are of a structural type as the portfolios that are available for this research are of this type (see Chapter 2).
Section 5.3: We address explanatory factors for estimating LGDs, which can influence the accuracy of an LGD model and suggest an approach to incorporate the uncertainty within our proposed alternative approach, which determines the implied AR of an LGD model.
Section 5.4: We summarize and conclude with the answer to the Sub-Research Question 4. We also argue why, in our opinion, our developed approach helps with un- derstanding the differences between different realized ARs between portfolios despite the limitations of the approach due to data issues.