Scenario Analysis in Risk Management

Theory and Practice in Finance

Business & Finance, Economics, Macroeconomics, Finance & Investing, Finance
Cover of the book Scenario Analysis in Risk Management by Bertrand K. Hassani, Springer International Publishing
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Author: Bertrand K. Hassani ISBN: 9783319250564
Publisher: Springer International Publishing Publication: October 26, 2016
Imprint: Springer Language: English
Author: Bertrand K. Hassani
ISBN: 9783319250564
Publisher: Springer International Publishing
Publication: October 26, 2016
Imprint: Springer
Language: English

This book focuses on identifying and explaining the key determinants of scenario analysis in the context of operational risk, stress testing and systemic risk, as well as management and planning. Each chapter presents alternative solutions to perform reliable scenario analysis. The author also provides technical notes and describes applications and key characteristics for each of the solutions. In addition, the book includes a section to help practitioners interpret the results and adjust them to real-life management activities. Methodologies, including those derived from consensus strategies, extreme value theory, Bayesian networks, Neural networks, Fault Trees, frequentist statistics and data mining are introduced in such a way as to make them understandable to readers without a quantitative background. Particular emphasis is given to the added value of the implementation of these methodologies. 

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This book focuses on identifying and explaining the key determinants of scenario analysis in the context of operational risk, stress testing and systemic risk, as well as management and planning. Each chapter presents alternative solutions to perform reliable scenario analysis. The author also provides technical notes and describes applications and key characteristics for each of the solutions. In addition, the book includes a section to help practitioners interpret the results and adjust them to real-life management activities. Methodologies, including those derived from consensus strategies, extreme value theory, Bayesian networks, Neural networks, Fault Trees, frequentist statistics and data mining are introduced in such a way as to make them understandable to readers without a quantitative background. Particular emphasis is given to the added value of the implementation of these methodologies. 

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