A First Course in Model Validation and Model Risk Management
- 1st Edition - January 1, 2026
- Authors: Jonathan Schachter, Martin Goldberg, Chandrakant Maheshwari
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 4 6 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 7 4 7 - 5
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-stud… Read more
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.
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.
- Offers practical concepts for learning model validation and model risk management
- Explains how to use Python-based models to assess and manage model risk
- Covers the US gold standard of model risk, "Federal Reserve Board SR 11-7", including testing inputs, testing outputs, benchmarking, outcomes analysis, third party models, and compensating controls
- Discusses model governance, including model inventory , risk ratings, and the three lines of defense
- Provides Instructor Manuals for qualified instructors via https://www.educate.elsevier.com/book/details/9780443337468
Advanced undergraduate and graduate students in Finance, Business, and Economics
Foundations and Applications of Game Theory
1. Applications of Game Theory in Artificial Intelligence: A Review
2. Applications of Game Theory in Climate Change Studies: A Review
3. A Review on the Applications of Game Theory in Environmental Health
4. Applications of game theory in renewable energy studies: A review
5. Tourist-resident interactions in evolutionary games: tourism and sustainability
6. Exploring the Evolution and Impact of Learning-Driven Game Theory for AI: A Bibliometric Analysis
Game Theory in Learning and AI Systems
7. Evolutionary Game Dynamics of Learning in Neural Networks Through Replicator Equations
8. Game-Based Ensemble Learning for Classifying Multi-Class Problems
9. Fair Incentive Allocation in Vertical Federated Learning Using Nucleolus
Game Theory and Explainability in AI
10. Several Perspectives on Explainable AI in Medicine: Game Theory Integrated Learning
11. Inverse Game Theory for Preference Learning in Generative AI Systems: A Computational
Complexity Framework
12. MYerson Additive Explanations on Graphs (MYER): Advancement of Explainable Artificial Intelligence Using a Graphical Approach
Mathematical Models and Adaptive Algorithms
13. Integrating Game-Theoretic Learning with AI for Lung Cancer Diagnosis and Risk Prediction
14. Truth as Geometry: A Topological Approach to Logic, Uncertainty, and AI Reasoning
15. Pursuit–Evasion Differential Games with Gronwall-Type Constraints: A Theoretical Study
16. Pursuit-Evasion Game under Lawden-Type Constraints
17. Guaranteed Pursuit Time of a Linear Pursuit Differential Game with a Mixed Constraints on Players’
Control Functions
18. Adaptive Control of Opinion Dynamics on a Social Network with a Principal
19. An Enhanced K-Means Clustering Approach: NBK-means Algorithm
1. Applications of Game Theory in Artificial Intelligence: A Review
2. Applications of Game Theory in Climate Change Studies: A Review
3. A Review on the Applications of Game Theory in Environmental Health
4. Applications of game theory in renewable energy studies: A review
5. Tourist-resident interactions in evolutionary games: tourism and sustainability
6. Exploring the Evolution and Impact of Learning-Driven Game Theory for AI: A Bibliometric Analysis
Game Theory in Learning and AI Systems
7. Evolutionary Game Dynamics of Learning in Neural Networks Through Replicator Equations
8. Game-Based Ensemble Learning for Classifying Multi-Class Problems
9. Fair Incentive Allocation in Vertical Federated Learning Using Nucleolus
Game Theory and Explainability in AI
10. Several Perspectives on Explainable AI in Medicine: Game Theory Integrated Learning
11. Inverse Game Theory for Preference Learning in Generative AI Systems: A Computational
Complexity Framework
12. MYerson Additive Explanations on Graphs (MYER): Advancement of Explainable Artificial Intelligence Using a Graphical Approach
Mathematical Models and Adaptive Algorithms
13. Integrating Game-Theoretic Learning with AI for Lung Cancer Diagnosis and Risk Prediction
14. Truth as Geometry: A Topological Approach to Logic, Uncertainty, and AI Reasoning
15. Pursuit–Evasion Differential Games with Gronwall-Type Constraints: A Theoretical Study
16. Pursuit-Evasion Game under Lawden-Type Constraints
17. Guaranteed Pursuit Time of a Linear Pursuit Differential Game with a Mixed Constraints on Players’
Control Functions
18. Adaptive Control of Opinion Dynamics on a Social Network with a Principal
19. An Enhanced K-Means Clustering Approach: NBK-means Algorithm
- Edition: 1
- Published: January 1, 2026
- Language: English
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Jonathan Schachter
Jonathan Schachter holds a PhD from University of California, Berkeley, and has over 23 years of experience as a model risk professional, with a practical background in market risk, portfolio risk, operational risk, capital model risk, and AI risk. Currently Jonathan is CEO and Founder of Delta Vega Inc, and has previously held positions at Jefferies Financial and Citibank, among other firms.
Affiliations and expertise
CEO and Founder of Delta Vega Inc, USAMG
Martin Goldberg
Martin Goldberg holds a PhD from City University of New York, and is Vice President in Model Validation at Mizuho Americas. He has held past positions at Citigroup, Bloomberg, AIG, and Chase Manhattan Bank. He is also on the Board of Directors for Rutgers University (Newark), for their Master of Quantitative Finance (MQF) Program, and has presented widely at conferences and universities on topics related to model risk assessment.
Affiliations and expertise
Vice President in Model Validation, Mizuho Americas, USACM
Chandrakant Maheshwari
Chandrakant Maheshwari is a seasoned expert in model validation with over 20 years of experience in financial risk analytics. An alum of the Indian Institute of Technology, Delhi, he is also an avid blogger and regularly publishes articles on model validation, sharing his extensive knowledge and insights in the field.
Affiliations and expertise
First Vice President, Lead Model Validator in Flagstar Bank