COURSE OUTLINE
Credit Risk Management
1. General
School
School of Finance and Statistics
Academic Unit
Department of Banking and Financial Management
Level of Studies
Undergraduate
Course code
ΧΡΜΔΠΚ01
Semester
6th or 8th
Course Title
Credit Risk Management
Idependent Teaching Activities
Weekly Teaching Hours
Credits
Lectures
4
7,5
Course Type
Elective
Prerequite Courses
Language of Instruction and Examinations
Greek
Is the course offered to Erasmus Students?
No
Url (Eclass)
https://eclass.unipi.gr/modules/auth/courses.php?fc=64
2. Learning Outcomes
Learning Outcomes
After completing the teaching cycle of the course, students will be able to:
- Understand the concepts of credit risk and market risk of financial institutions and the importance of measuring and managing these risks.
- Understand regulatory frameworks and capital requirements of supervisory authorities (e.g. Basel, ECB Guide to Internal Models – “EGIM”).
- Understand the trade-off between returns and risk.
- Understand how to calculate the volatility and correlation of portfolio’s instruments.
- Be able to calculate credit risk and market risk through the standardized approach.
- Understand how to estimate market risk using various statistical methods (parametric, non-parametric and semi-parametric).
- Understand how to estimate the credit risk using the measures of the expected loss (ECL), i.e. the probability of default (PD), the loss given default (LGD) as well as exposure at default (EAD).
General Competences
- Search, analysis and synthesis of data and information, using the necessary technologies
- Decision making
- Autonomous working assignment
- Grouped working assignment and case studies
3. Syllabus
- Introduction to the concepts of financial risks (focusing on credit risk and market risk).
- The role of regulatory frameworks and supervisory authorities in financial institutions.
- Trade-off between returns and risk.
- Introduction to the estimation of volatility and correlation.
- Estimation and prediction of volatility (EWMA, GARCH models) using a programming language (e.g., MATLAB)
- Calculation of market risk measures (e.g., VaR, Expected Shortfall) with parametric, non-parametric and semi-parametric methods using a programming language (e.g., MATLAB).
- Market Risk Backtesting using a programming language (e.g., MATLAB).
- Introduction to credit risk and credit ratings.
- Theoretical background and estimation of the expected loss (ECL), using the probability of default (PD), the loss given default (LGD) as well as the exposure at default (EAD).
- Empirical applications on the measurement of credit risk and market risk using the standardized approach and internal models based on the regulatory framework.
4. Teaching and Learning Methods - Evaluation
Delivery
Face-to-face
Use of Information and Communications Technology
Laboratory education
Teaching Methods
Activity
Semester Workload
Lectures
52
Independent Study
57,5
In – class exercises
26
Study and analysis of bibliography
26
Course Total
187,5
Student Performance Evaluation
Students will be assessed through a written exam, a working assignment and participation in the case studies.
5. Attached Bibliography
Suggested Bibliography
- Altman, E. I. (2000). Predicting financial distress of companies: revisiting the Z-score and ZETA models. Stern School of Business, New York University, 9-12.
- Bauwens, L., Laurent, S. and Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of Applied Econometrics, 21(1), 79-109.
- Christoffersen, P. (2011). Elements of Financial Risk Management, Academic Press.
- Degiannakis, S. and Xekalaki, E. (2010). ARCH Models for Financial Applications, Wiley, New York.
- Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157-168.
- Hull, J. (2015). Options, futures, and other derivatives, 9th edition, Prentice Hall.
- Jorion, P. (2006). Value at risk. McGraw-Hill.
- Jorion, P. (2009). Risk Management Lessons from the Credit Crisis. European Financial Management, 15(5), 923-933.