# CFRM 542: Credit Risk Management

> Reading Material:
>
> * *Measuring and Managing Credit Risk,* Servigny, Arnaud de and Renault, Olivier, 2004
> * *Applied Logistic Regression,* Hosmer, David W and Lemeshow, Stanley, 2000

This course covers the theory, applications and computational methods for credit risk measurement and management. It also discusses the statistical and mathematical modeling of credit risk, emphasizing numerical methods and R programming. The methods include logistic regression, Monte Carlo simulation, and portfolio cash flow modeling. It covers default risk regression, analytics, and portfolio models of credit risk.

1. Use regression analysis to investigate credit risk using R
   1. Individual borrower level analysis
   2. Firm or portfolio level analysis
2. Assess goodness-of-fit of new or existing credit models
3. Model and understand Credit Default Swaps (CDS) and credit spreads in bond markets
4. Evaluate the credit structure in asset backed securities
5. Build and understand portfolio models for credit risk
   1. Credit risk economic capital
   2. Basel framework for bank credit risk regulatory capital
6. Understand current events relating to credit risk, banking, and finance


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