The first systematic treatment of model risk, this book provides the tools needed to quantify and assess the impact of model uncertainty. It will be essential for all those working in portfolio theory and the theory of financial and engineering risk, for practitioners in these areas, and for graduate courses on risk bounds and model uncertainty.
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.
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'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
Muu info
Develop the tools to quantify model risk, to study its effects in finance, insurance, and engineering, and to reduce it.
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.
Ludger Rüschendorf is Professor of Mathematics at the University of Freiburg. He is author of more than 200 research papers and a number of textbooks, in a variety of subjects in probability, statistics, analysis of algorithms as well as in risk analysis and in mathematical finance. A main topic in his research is the modeling and analysis of dependence structures. Steven Vanduffel is Professor in Risk Management at the Solvay Business School at Vrije Universiteit Brussel. He has authored papers for leading journals including 'Journal of Risk and Insurance,' 'Finance and Stochastics,' 'Mathematical Finance,' and 'Journal of Econometrics.' He has won prizes including the Robert I. Mehr Award (2022), the Robert C. Witt Award (2018), and the Redington Prize (2015). Carole Bernard is Professor in Finance at Grenoble Ecole de Management and Vrije Universiteit Brussel. She has published articles in leading journals in finance, insurance, operations research, and risk management, including 'Management Science,' 'Journal of Risk and Insurance,' 'Journal of Banking and Finance,' and 'Mathematical Finance.'
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