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Full Description
A First Course in Model Validation and Model Risk Management offers robust coverage for current and future financial engineers. Useful as part of a masters program, for self-study, or as a valuable reference, the textbook explains in step-by-step, practical terms how mathematical models owned by financial institutions are essential to their public activities, including sales, trading, risk management, and internal audits. Like a diverse fleet of cars maintained by a rental car location, a bank must make sure customers can "drive" any of its models for a specific financial product. The book covers both pricing and risk models. Chapters consider modeling basics, marked-to-market and marked-to-model asset classes, market risk, credit risk, portfolio risk, operational risk, capital model risk, and financial crime, along with machine learning/AI.
To support course use and practical applications, the text provides examples in Python throughout, as well as an appendix containing homework problems for all chapters, further supported by an ftp site for data and sample code. Additional appendices cover global model risk management, and a refresher in statistics.
Contents
PART I. KEY CONCEPTS OF MODEL RISK MANAGEMENT
1. Introductory material
2. Model Basics
3. Standards
4. Techniques
PART II. VALIDATION OF PRICING AND MARKET RISK MODELS
5. Marked-to-Market Asset Classes
6. Marked-to-Model Asset Classes
7. Market Risk I: Statistical Measures
8. Market Risk II: Stress Testing
PART III. VALIDATION OF CREDIT MODELS
9. Issuer Credit Risk
10. Counterparty Credit Risk
11. Correlation Credit Risk
PART IV. VALIDATION OF OTHER MODELS AND GOVERNANCE
12. Portfolio Risk
13. Operational Risk
14. Capital Model Risk
15. Artificial Intelligence: Models, Enhancements to MRM, and Regulation
16. Miscellaneous topics in Model Risk
17. Model Governance
