A First Course in Model Validation and Model Risk Management - Edition 1 - By Jonathan Schachter, Martin Goldberg and Chandrakant Maheshwari Elsevier Educate

Purchase textbook

New

A First Course in Model Validation and Model Risk Management,

Edition 1

By Jonathan Schachter, Martin Goldberg and Chandrakant Maheshwari

Publication Date: 01 Jan 2026

0 Reviews

Not yet published

No accessibility information available.

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.

Key Features

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

About the author

By Jonathan Schachter, CEO and Founder of Delta Vega Inc, USA; Martin Goldberg, Vice President in Model Validation, Mizuho Americas, USA and Chandrakant Maheshwari, First Vice President, Lead Model Validator in Flagstar Bank

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

ISBN: 9780443337468

Page Count: 400

Retail Price (USD) :

Advanced undergraduate and graduate students in Finance, Business, and Economics

Related Titles

Book Details

Key Features

About the author