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 "e;drive"e; 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. - 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, "e;Federal Reserve Board SR 11-7"e;, 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

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 "e;drive"e; 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. - 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, "e;Federal Reserve Board SR 11-7"e;, 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

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 "e;drive"e; 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. - 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, "e;Federal Reserve Board SR 11-7"e;, 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