"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"--
"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"--
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.
Inhaltsangabe
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.
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.
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