Model Risk Management
Model Risk Management
Steven Vanduffel, Vrije Universiteit Brussel
Carole Bernard, Grenoble Ecole de Management
This book provides the first systematic treatment of model risk, outlining the tools needed to quantify model uncertainty, to study its effects, and, in particular, to determine the best upper and lower risk bounds for various risk aggregation functionals of interest. Drawing on both numerical and analytical examples, this is a thorough reference work for actuaries, risk managers, and regulators. Supervisory authorities can use the methods discussed to challenge the models used by banks and insurers, and banks and insurers can use them to prioritize the activities on model development, identifying which ones require more attention than others. In sum, it is essential reading for all those working in portfolio theory and the theory of financial and engineering risk, as well as for practitioners in these areas. It can also be used as a textbook for graduate courses on risk bounds and model uncertainty.
- Investigates numerous relevant examples of model uncertainty faced in finance and insurance
- Fully develops the necessary underlying concepts to build a fundamental understanding of basic principles and methods in risk analysis
- Written in a readable non-formalistic style, accessible to readers from academia with interest in risk analysis as well as to practitioners with a quantitative background
Reviews & endorsements
'Written by three of the foremost experts in the field, Model Risk Management is the definitive textbook on bounding aggregate or portfolio risks in the face of partial information about their probabilistic structure, a problem that has applications in many areas of financial risk management, and beyond.' Alexander McNeil, University of York
'This phenomenal reference text is the first to provide a systematic treatment of model uncertainty in a quantitative risk management context. It offers a broad array of methods for determining optimal bounds for portfolio VaR and other risk aggregation measures when only partial information is available about the model structure. Every actuary, quant, and regulator should own this book and apply its lessons in the insurance and financial services industry.' Christian Genest, FRSC, Canada Research Chair, McGill University
Product details
No date availableAdobe eBook Reader
9781009367202
0 pages
Table of Contents
- Introduction
- Part I. Risk Bounds for Portfolios Based on Marginal Information:
- 1. Risk bounds with known marginal distributions
- 2. Rearrangement algorithm
- 3. Dual bounds
- 4. Asymptotic equivalence results
- Part II. Additional Dependence Constraints:
- 5. Improved standard bounds
- 6. VaR bounds with variance constraints
- 7. Distributions specified on a subset
- Part III. Additional Information on the Structure:
- 8. Additional information on functionals of the risk vector
- 9. Partially specified risk factor models
- 10. Models with a specified subgroup structure
- Part IV. Risk Bounds Under Moment Information:
- 11. Bounds on VaR, TVaR, and RVaR under moment information
- 12. Bounds for distortion risk measures under moment information
- 13. Bounds for VaR, TVaR, and RVaR under unimodality constraints
- 14. Moment bounds in neighborhood models
- References
- Index.
