The recovery performance of a portfolio of non-performing loans (NPL) 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 more 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.

Unsupervised classification of NPLs recovery curves / Carleo, Alessandra; Rocci, Roberto. - (2023), pp. 311-316. (Intervento presentato al convegno IES2023 tenutosi a Pescara).

Unsupervised classification of NPLs recovery curves

Rocci Roberto
2023

Abstract

The recovery performance of a portfolio of non-performing loans (NPL) 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 more 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.
2023
IES2023
K-means; NPL, recovery curve; censored data; smoothing
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Unsupervised classification of NPLs recovery curves / Carleo, Alessandra; Rocci, Roberto. - (2023), pp. 311-316. (Intervento presentato al convegno IES2023 tenutosi a Pescara).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1689686
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