Book Details

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.

Key Features

• 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