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Beschreibung
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.
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.
Über den Autor
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.
Inhaltsverzeichnis
1. Introductory Material
2. Model Basics
3. Standards
4. Techniques
5. Marked-to-Market Asset Classes
6. Marked-to-Model Asset Classes
7. Market Risk I: Statistical Measures
8. Market Risk II: Stress Testing
9. Issuer Credit Risk
10. Counterparty Credit Risk
11. Correlation Credit Risk
12. Portfolio Risk
13. Operational Risk
14. Capital Model Risk
15. Artificial Intelligence Risk
16. Miscellaneous Topics in Model Risk
17. Model Governance
Details
Erscheinungsjahr: 2026
Fachbereich: Betriebswirtschaft
Genre: Importe, Wirtschaft
Rubrik: Recht & Wirtschaft
Medium: Taschenbuch
Inhalt: Einband - fest (Hardcover)
ISBN-13: 9780443337468
ISBN-10: 0443337462
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Schachter, Jonathan
Goldberg, Martin
Maheshwari, Chandrakant
Hersteller: Elsevier LTD
Academic Press
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 230 x 192 x 35 mm
Von/Mit: Jonathan Schachter (u. a.)
Erscheinungsdatum: 20.04.2026
Gewicht: 1,172 kg
Artikel-ID: 135423252

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