Model Risk Management
Risk Bounds under Uncertainty

Develop the tools to quantify model risk, to study its effects in finance, insurance, and engineering, and to reduce it.

Ludger Rüschendorf (Author), Steven Vanduffel (Author), Carole Bernard (Author)

9781009367165, Cambridge University Press

Hardback, published 25 January 2024

345 pages
25.1 x 17.4 x 2.4 cm, 0.78 kg

'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

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.

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.

Subject Areas: Optimization [PBU]