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Modeling Credit Default: A Portfolio-Based Comparison of CreditMetrics and CreditRisk

Francesca Vogliotti

Modeling Credit Default: A Portfolio-Based Comparison of CreditMetrics and CreditRisk.

Rel. Patrizia Semeraro. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2023

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Credit risk is the possibility of losing a lender holds, due to a risk of default on a debt that may arise from a borrower failing to make required payments. The modelling of this risk is an indispensable tool utilized by financial institutions worldwide - including banks, insurance companies and investment firms - to effectively measure, control and manage the risk associated with lending. The sphere of credit risk though is by no means confined to just loans or borrowed money; it extends to other areas of financial transactions, including bonds, derivatives, and other financial products. For this reason, this element of risk has triggered a wave of interest from various stakeholders ranging from academia to industry players, regulators and policy makers. The objective of this thesis is to evaluate and compare the credit risk modelling methods of CreditMetrics and CreditRisk+. The methodological approaches include the implementation of both models with different techniques, taking care of assuring a consistent parameterization, and an in-depth comparison of the two, both in case of independent and dependent exposures. It is revealed through different scenarios that both models yield consistent results while having diverse strong points. CreditMetrics is found to provide detailed modelling incorporating credit migrations and coupons, making it the ideal choice for institutions focusing on the in-depth understanding of each debtor's profile. Conversely, CreditRisk+ demonstrates efficiency in dealing large debtor portfolios, making it a suitable choice for institutions focusing on retail settings. It highlights that institutions' choice of the model should be guided by their specific needs, whether it is a complex understanding of each debtor or the management of large portfolios.

Relators: Patrizia Semeraro
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 67
Corso di laurea: Corso di laurea magistrale in Ingegneria Matematica
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/28703
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