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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.
List of contents
Introduction and Environment.- Environment.- The Information set.- The Consensus Approach.- Tilting Strategy - Using Probability Distribution Properties.- Leveraging Extreme Value Theory.- Bayesian Networks.- Articial neural network to serve scenario analysis purposes.- Fault Trees and variations.- Forward looking underlying information: Working with time series.- Dependencies and relationships between variables.
About the author
Dr. Bertrand Hassani is Global Head of Research and Innovation - Risk Methodology, Grupo Santander, and Associate Researcher Université Paris 1 Panthéon Sorbonne
Summary
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.
