Scenario Analysis in Risk Management
Theory and Practice in Finance
Business & Finance, Economics, Macroeconomics, Finance & Investing, Finance
| 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.
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.
