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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

The goal of the course “Credit Risk Measurement and Management” is the in-depth examination on the measurement and management of risks faced by financial institutions and the financial system. The main type of risks that are being analysed during the course are the Market Risk and the Credit Risk. The presentation of these risks is being done through the use of realistic examples.

With the successful completion of the course, a student:

  • Will be aware of how financial institutions operate, as well as the risks they face.
  • Will be able to understand the theoretical framework and apply the statistical tools to measure the different types of risk.
  • Will be able to solve risk management problems by effectively using the technical approaches used by financial institutions to manage risk.
General Competences
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology.
  • Decision-making.
  • Working in an international environment.
  • Production of free, creative and inductive thinking

3. Syllabus

  1. Introduction
    1. Theoretical Introduction
      • The importance of risk management for a financial institution (internal management, supervisory authorities)
      • Risk – return trade – off
      • Financial institutions’ management incentives and the importance of supervisory control
    2. Technical Introduction
      • Volatility (definitions, assumptions, valuation methods, possible problems)
      • Correlation (definitions, assumptions, valuation methods, possible problems)
      • Copulas (definitions, assumptions, valuation methods, possible problems)
  2. Market Risk
    • Risk Management: Standalone & Cumulative
    • Standalone
      • Derivatives – Greek letters
    • Cumulative
      • RiskMetrics – Value at Risk (VaR)
      • Expected Shortfall
      • VaR estimation:
        • From historical data – Historical Simulation
        • Theoretical model
        • Monte Carlo simulation
  3. Credit Risk
    • Credit Ratings
    • Altman’s Z-score
    • Default probability based on historical data
    • Default recovery rates
    • Default probability estimation (bond prices)
    • Default probability estimation: historical data vs bond prices
    • Default probability estimation (stock prices)
    • Distance to Default (Merton’s Model)
    • Credit VaR
    • Credit Risk Plus
    • CreditMetrics

4. Teaching and Learning Methods - Evaluation

Delivery

Face-to-face

Use of Information and Communications Technology

Teaching through Microsoft Powerpoint slides, Contact with students through email

Teaching Methods

Activity

Semester Workload

Lectures
52
Independent Study
83,5
In – class exercises
26
Study and analysis of bibliography
26
Course Total
187,5

Student Performance Evaluation

Final written exam (100%):

  • –  Multiple choice questions
  • –  Risk valuation exercises

5. Attached Bibliography

Suggested Bibliography

– Suggested bibliography:

  • Teacher’s note (eclass.unipi.gr)
  • Financial Institutions Management: A Risk Management Approach, Anthony Saunders & Marcia Millon Cornett, Broken Hill Publishers, 2017, 2014 8th edition translation

 

  • Acharya, V., Drechsler, I., & Schnabl, P. (2014). A pyrrhic victory? Bank bailouts and sovereign credit risk. The Journal of Finance, 69(6), 2689-2739.
  • Adrian, Tobias; Brunnermeier, Markus K., CoVaR, American Economic Review, Volume 106, Number 7, July 2016, pp. 1705-1741 (37).
  • Ang, A., & Longstaff, F. A. (2013). Systemic sovereign credit risk: Lessons from the US and Europe. Journal of Monetary Economics, 60(5), 493-510.
  • 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.
  • Saunders & M.M. Cornett, Financial Institutions Management: A Risk Management Approach, McGraw Hill
  • Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of applied econometrics, 21(1), 79-109.
  • Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. The Review of Financial Studies, 21(3), 1339-1369.
  • Brunnermeier, M. K. (2009). Deciphering the liquidity and credit crunch 2007-2008. Journal of Economic perspectives, 23(1), 77-100.
  • Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of economic perspectives, 15(4), 157-168.
  • De Haan, L., & Ferreira, A. (2007). Extreme value theory: an introduction. Springer Science & Business Media.
  • Frey, R., & McNeil, A. J. (2002). VaR and expected shortfall in portfolios of dependent credit risks: conceptual and practical insights. Journal of banking & finance, 26(7), 1317-1334.
  • Hansen, L. P. (2013). Challenges in identifying and measuring systemic risk. In Risk topography: Systemic risk and macro modeling (pp. 15-30). University of Chicago Press.
  • He, Z., & Xiong, W. (2012). Rollover risk and credit risk. The Journal of Finance, 67(2), 391-430.
  • Hull, J. C., & White, A. D. (2000). Valuing credit default swaps I: No counterparty default risk. The Journal of Derivatives, 8(1), 29-40.
  • Hull, J. C., & White, A. D. (2000). Valuing credit default swaps I: No counterparty default risk. The Journal of Derivatives, 8(1), 29-40.
  • Hull, J. C., Risk Management and Financial Institutions, Pearson Education
  • John C. Hul, Options, Futures, and Other Derivatives, Pearson Education
  • Jorion, P. (2000). Value at risk.
  • Longstaff, F. A., Pan, J., Pedersen, L. H., & Singleton, K. J. (2011). How sovereign is sovereign credit risk?. American Economic Journal: Macroeconomics, 3(2), 75-103.
  • Manso, G. (2013). Feedback effects of credit ratings. Journal of Financial Economics, 109(2), 535-548.
  • Morgan, J. P. (1997). Creditmetrics-technical document. JP Morgan, New York.
  • Viral V. Acharya, Lasse H. Pedersen, Thomas Philippon and Matthew Richardson, Measuring Systemic Risk, Review of Financial Studies (2017) 30 (1): 2-47
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