Unit 4103: Advanced Credit Risk and Portfolio Management

1. Credit Risk Assessment Models

1.1 Introduction to Credit Risk

• Definition and importance of credit risk assessment
• Types of credit risk

1.2 Statistical Models

• Logistic regression
• Linear discriminant analysis
• Credit risk scoring techniques

1.3 Machine Learning Techniques

• Decision trees and random forests
• Support vector machines
• Neural networks and deep learning
• Model validation and overfitting

1.4 Probabilistic Models

• Probability of default (PD), loss given default (LGD), exposure at default (EAD)
• Credit portfolio models (e.g., CreditMetrics, KMV)

2. Portfolio Management in Credit

2.1 Portfolio Diversification

• Benefits and risks of diversification
• Measuring correlation and concentration risk

2.2 Risk Management Techniques

• Credit risk mitigation (collateral, guarantees, credit derivatives)
• Credit limits and exposure management

2.3 Performance Evaluation

• Risk-adjusted return measures (RAROC, Sharpe ratio)
• Portfolio optimization techniques

3. Credit Derivatives and Structured Products

3.1 Overview of Credit Derivatives

• Credit default swaps (CDS): structure, pricing, and applications
• Credit-linked notes (CLNs)

3.2 Structured Credit Products

• Collateralized debt obligations (CDOs): tranching and risk transfer
• Synthetic CDOs and their market role

3.3 Role in Credit Risk Management and Trading

• Hedging credit risk with derivatives
• Speculation and arbitrage strategies

4. Stress Testing and Scenario Analysis

4.1 Purpose and Importance

• Regulatory requirements (e.g., Basel III stress testing guidelines)
• Identifying vulnerabilities in credit portfolios

4.2 Designing Stress Tests

• Macro-economic scenarios
• Idiosyncratic shocks

4.3 Scenario Analysis Methods

• Sensitivity analysis
• Reverse stress testing

4.4 Best Practices and Implementation Challenges

5. Behavioral Finance in Credit Decision-Making

5.1 Cognitive Biases Affecting Credit Assessment

• Overconfidence, anchoring, confirmation bias

5.2 Heuristics in Lending Decisions

• Representativeness, availability heuristic

5.3 Emotional and Social Factors

• Impact on risk perception and decision-making

5.4 Strategies to Mitigate Behavioral Risks

6. Sustainable Finance and ESG Integration in Credit

6.1 Introduction to ESG Factors

• Environmental, social, and governance criteria

6.2 Integrating ESG into Credit Analysis

• ESG scoring and rating methodologies
• Impact investing principles

6.3 Risk Mitigation Through Sustainable Finance

• Identifying ESG risks in credit portfolios
• Regulatory trends and reporting requirements

7. Credit Rating Agencies and Credit Scoring

7.1 Role of Credit Rating Agencies

• Major global agencies and their influence
• Rating process and criteria

7.2 Credit Scoring Models

• Traditional scoring methodologies
• Advances with data analytics and machine learning

7.3 Challenges and Criticisms

• Rating accuracy and conflicts of interest

8. Regulatory Frameworks in Credit Management

8.1 Basel III Requirements

• Capital adequacy and risk-weighted assets
• Credit risk buffers and leverage ratios

8.2 Stress Testing Guidelines

• Regulatory expectations and compliance

8.3 Accounting Standards

• IFRS 9 and loan loss provisioning

8.4 Compliance with AML and KYC

• Importance for credit management
• Procedures and controls