Managing Credit Portfolio Risk With GFMI
Our credit portfolio risk management course begins with analyzing the main factors that are associated with credit risk, including Exposure at Default, Loss Given Default, and Probability of Default. Once credit exposures are aggregated into a portfolio, correlation plays an important role – and this course evaluates how the benefits and risks of correlation are measured.
There are several different approaches to take when modeling credit risk, such as structural versus reduced form among others. During this course, these approaches are analyzed and the types of models used on the Street are discussed, including KMV/Merton, Creditmetrics, Creditrisk+, among others. The new regulatory environment, including the analytical approaches to calculating credit risk capital allocations that are required by Basel III and Dodd-Frank will be discussed. Finally, practical approaches to monitoring and controlling credit risk will be presented.
Credit Portfolio Risk Management Objectives
After completing this course, participants will be able to:
- Identify the main components of default risk:
- Exposure at Default (EAD)
- Loss Given Default (LGD)
- Probability of Default (PD)
- Analyze and measure the impact of correlation on credit portfolio risk exposures
- Compare and contrast different approaches to credit risk modeling
- Discuss different models frequently used on the Street
- Discuss the current regulatory environment regarding capital adequacy for credit risk
Prerequisites: Fundamental understanding of statistics and credit risk analysis
Program Level: Intermediate
Advance Preparation: None
Recommended CPE Credits: 7
Our Credit Risk Portfolio Management Course is broken down into four sessions.
- Session 1: Introduction to Credit Risk Management will define expected and unexpected losses for participants, as well as default risk. The distinguishing factors between credit and default risk will be illuminated and credit VaR will be defined. A discussion on the components of default risk and an analysis of credit loss distribution (or density function) will also occur.
- By the end of Session 2: Modeling Credit Risk, participants will be able to identify the risks at the credit portfolio level, as well as explain and calculate the credit migration approach (CreditMetrics) including VaR for a single instrument as well as at the portfolio level. They will be able to explain the contingent claims approach to measuring credit risk (Merton’s model), as well as credit risk via CreditEdge Moody’s KMV approach. These credit risk models will be compared and contrasted.
- Session 3: Default Dependence and Credit Portfolio Risk will explain why credit returns are not like market returns. Participants will discuss default dependence and the tail of portfolio loss distribution. By this session’s end, they will be able to identify the Gaussian Latent Variable Model and explain Monte Carlo Simulation of Credit Losses.
- The final session of our Credit Risk Portfolio Management Course, Session 4: Credit VaR, Expected Shortfall, and Economic Capital will teach participants how to identify the credit policy, procedures, and process. They will learn how to understand risk measures, including Credit VaR, CS01/CR01, market value shocks, and scenario analysis, as well as how to identify limit structures for controlling risk.