A First Course in Model Validation and Model Risk Management
- 1st Edition - January 1, 2026
- Imprint: Academic Press
- 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 explains in step-by-step, practical terms how mathematical models owned by financial institutions are essential to the… Read more
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A First Course in Model Validation and Model Risk Management explains in step-by-step, practical terms how mathematical models owned by financial institutions are essential to their public (sales, trading, risk management, and internal audit) and private (merger and acquisition, and IPO) activities. Like a diverse fleet of cars maintained by a rental car location, a bank must make sure customers can “drive” any of its models in seeking a profit or hedge in a specific financial product.
The book is divided into three sections on conventional pricing and risk models, including risk-neutral and historical measures. Chapters consider modeling basics, marked-to-market asset classes, market risk, credit risk, portfolio risk, operational risk, capital model risk, and financial crime, along with machine learning, AI, and Python specific modeling and risk assessment techniques. Problems sets, video examples, sample Python code, and an instructor manual are offered on companion and instructor sites to support learning and provide an opportunity to put concepts into practice. A refresher in statistics and an abbreviation glossary are included across two appendices.
The book is divided into three sections on conventional pricing and risk models, including risk-neutral and historical measures. Chapters consider modeling basics, marked-to-market asset classes, market risk, credit risk, portfolio risk, operational risk, capital model risk, and financial crime, along with machine learning, AI, and Python specific modeling and risk assessment techniques. Problems sets, video examples, sample Python code, and an instructor manual are offered on companion and instructor sites to support learning and provide an opportunity to put concepts into practice. A refresher in statistics and an abbreviation glossary are included across two appendices.
- Offers practical instruction in model validation and a model risk management, with clear explanations and practice problems
- Teaches how to use machine learning, AI, and Python-based models to assess and manage risk
- Covers the gold standard of model risk “SR 11-7”, used by the US Federal Government: testing inputs, testing outputs, benchmarking, outcomes analysis, governance, inventory, third party products, and compensating controls
- Includes problems sets and sample Python code on a companion website, as well as companion videos and an online instructor manual
Advanced undergraduate and graduate students in Finance, Business, and Economics
PART I. KEY CONCEPTS OF MODEL RISK MANAGEMENT
1. Introductory material
2. Model Basics
3. Standards
4. Techniques
PART II. VALIDATION OF PRICING MODELS
5. Marked-to-Market Asset Classes
6. Marked-to-Model Asset Classes
PART III: VALIDATION OF RISK MODELS
7. Market Risk
8. Credit Risk
9. Portfolio Risk
10. Operational Risk
11. Capital Model Risk
12. Artificial Intelligence Model Risk
13. Miscellaneous topics in Model Risk
14. References
15. Appendix: Statistics Refresher
16. Appendix: Glossary of Abbreviations
1. Introductory material
2. Model Basics
3. Standards
4. Techniques
PART II. VALIDATION OF PRICING MODELS
5. Marked-to-Market Asset Classes
6. Marked-to-Model Asset Classes
PART III: VALIDATION OF RISK MODELS
7. Market Risk
8. Credit Risk
9. Portfolio Risk
10. Operational Risk
11. Capital Model Risk
12. Artificial Intelligence Model Risk
13. Miscellaneous topics in Model Risk
14. References
15. Appendix: Statistics Refresher
16. Appendix: Glossary of Abbreviations
- Edition: 1
- Published: January 1, 2026
- Imprint: Academic Press
- 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