University of Twente Student Theses

Login

Internal risk components validation : indicative benchmarking of discriminatory power for LGD models

Sproates, C.L. (2017) Internal risk components validation : indicative benchmarking of discriminatory power for LGD models.

[img]
Preview
PDF
1MB
[img] PDF
Restricted to Restricted

1MB
Abstract:In this study, we attempt to find a systematic approach to set indicative benchmark values for the discriminatory power of an LGD model measured via the Accuracy Ratio (AR), which is a summary statistic for the discriminatory power of a classification model. LGD models are relatively new compared to PD models, but the last years have seen a significant number of papers discussing the LGD model. Unfortunately, there is not yet an approach which indicates whether a model's performance is sufficient in terms of discriminatory power. For these reasons the bank found it difficult determining what a sufficient level of discriminatory power for an LGD model is. We review the method that has been developed by the bank for benchmarking discriminatory power via the AR, while we also develop and propose our own new approach to set indicative benchmarks. Our approach provides insights in how the AR varies under specific portfolio characteristics and illustrate that benchmarks should be determined dependent on these characteristics.
Item Type:Essay (Master)
Clients:
Rabobank, Utrecht, Nederland
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:31 mathematics, 85 business administration, organizational science
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:http://purl.utwente.nl/essays/71977
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page