Course Scope

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).

Course Outline

• 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.

Suggested Reading

• 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.