- Docente: Tiziano Bellini
- Credits: 6
- SSD: SECS-S/06
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Quantitative Finance (cod. 8854)
Learning outcomes
Credit risk is the main source of risk for a commercial bank, far more relevant then other risk types like market, interest, operational, etc.. and can have dramatic systemic effects, as an under estimation of credit risk is at the root of the financial and economic turmoil of the 2008. The course will provide a broad overview of credit risk management in (commercial) banking. First, we will review the methodologies behind the more widely used (structural and reduced form) models for individual credit risk components (EAD,PD,LGD); then we will analyse how credit risk is measured at portfolio level (expected and unexpected losses, risk aggregation and contributions) by the most popular metrics (the Moody’s KMV model, CreditMetrics™ and Credit Risk Plus™). Finally, we will address credit risk regulatory topics, both in terms of capital requirements (Basle II and III) and of accounting standards (IFRS9). The theoretical insight will be paired with practical exercises: some of these models and metrics will be applied to real credits data using the Python scientific stack.
Course contents
Credit risk is the main source of risk for a commercial bank, far more relevant then other risk types like market, interest, operational, etc.. and can have dramatic systemic effects, as an under estimation of credit risk is at the root of the financial and economic turmoil of the 2008. The course will provide a broad overview of credit risk management in (commercial) banking. We will analyze the methodologies behind the more widely used (structural and reduced form) models for individual credit risk components (EAD,PD,LGD). We will address credit risk with focus on the recently introduced accounting principle IFRS9 and CECL with some hints on regulatory Basel II perspective. The theoretical insight will be paired with practical exercises: some of these models and metrics will be applied to real credits data using R scientific stack.
Readings/Bibliography
IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide with Examples Worked in R and SAS. Academic Press - Inprint Elsevier. Author: Bellini Tiziano
https://www.elsevier.com/books/ifrs-9-and-cecl-credit-risk-modelling-and-validation/bellini/978-0-12-814940-9
Teaching tools
Credit retail practical datasets and software code developed in R
Office hours
See the website of Tiziano Bellini