Model Risk Management

Risk Bounds under Uncertainty

By: Ludger Ruschendorf, Steven Vanduffel, Carole Bernard

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Hardcover | 25 January 2024

At a Glance

Hardcover
346 Pages

Dimensions(cm)
24.41 x 16.99 x 2.06

Hardcover

RRP $199.95

$176.75

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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.

Industry Reviews

'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

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