A practical textbook for financial engineering students and model risk practitioners.
Book page and supporting materials. Purchase links and downloadable resources will be added as they become available.
Principles and Practices of Model Risk Management provides an end-to-end framework for designing, operating, and improving an MRM program aligned with current model risk management guidance and supervisory expectations, including the Federal Reserve's SR 26-2. It covers the three lines of defense and the full model lifecycle—from model development to use, performance monitoring, remediation, change management, and decommissioning.
We’ll publish a full table of contents and sample materials here.
Research notes and commentary extending themes from the book into current questions in financial markets, monetary policy, quantitative risk, AI, and model governance.
How higher long-term Treasury yields have tightened financial conditions, repriced long-duration assets, and complicated the case for further Federal Reserve rate increases amid supply-driven inflation.
Why the effective monetary stance should be evaluated across the yield curve, credit conditions, term premia, liquidity, and the Federal Reserve’s balance sheet—not only the overnight policy rate.
A supplemental chapter discussing how model risk management applies to generative AI, large language models, retrieval-augmented generation, and agentic AI systems.
Additional chapter study guides, case studies, Python notebooks, instructor materials, and supporting resources will be added as they become available.
For questions, corrections, or instructor requests:
Charlie Lu:
changnian.lu@gmail.com
Peter Russo:
plr27@columbia.edu
Instructor resources and additional teaching materials will be added as the book project develops.
© Charlie Lu and Peter Russo