Abstract
The recovery performance of a portfolio of Non-Performing Loans can be measured in terms of recovery rate and liquidation time jointly through a “recovery curve” representative of recovery rates over time. When portfolio heterogeneity is very high, it is informative to estimate more than just one curve by dividing the portfolio into several homogeneous subsets, i.e. clusters, and calculating a recovery curve for each of them. The aim of this work is to estimate the optimal portfolio partition and the smoothed recovery curves of each cluster by means of non-parametric statistical learning techniques.
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