http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
"A companion book to Fundamental Aspects of Operational Risk Modeling and Insurance Analytics: A Handbook of Operational Risk (2014), this book covers key mathematical and statistical aspects of the quantitative modelling of heavy tailed loss processes in operational risk and insurance settings. This book can add value to the industry by providing clear and detailed coverage of modelling for heavy tailed operational risk losses from both a rigorous mathematical as well as a statistical perspective. Few books cover the range of details provided both the mathematical and statistical features of such models, directly targeting practitioners. The book focuses on providing a sound understanding of how one would mathematically and statistically model, estimate, simulate and validate heavy tailed loss process models in operational risk. Coverage includes advanced topics on risk modelling in high consequence low frequency loss processes. This features splice loss models and motivation for heavy tailed risk processes models. The key aspects of extreme value theory and their development in loss distributional approach modelling is considered. Classification and understanding of different classes of heavy tailed risk process models is discussed, this leads into topics on heavy tailed closed form loss distributional approach models and flexible heavy tailed risk models such as a-stable and tempered stable models. The remainder of the chapters covers advanced topics on risk measures and asymptotics for heavy tailed compound process models. The finishing chapter covers advanced topics including forming links between actuarial compound process recursions and monte carlo numerical solutions for capital and risk measure estimations"--
"Covers key mathematical and statistical aspects of the quantitative modelling of heavy tailed loss processes in operational risk and insurance settings. Includes advanced topics on risk modelling in high consequence low frequency loss processes, key aspects of extreme value theory, and classification of different classes of heavy tailed risk process models. Primarily developed for advanced risk management practitioners and quantitative analysts. Suitable as a core reference for an advanced mathematical or statistical risk management masters course or a PhD research course on risk management and asymptotics"--
Title Page; Copyright; Table of Contents; Dedication; Preface; Acknowledgments; Acronyms; Symbols; List of Distributions; Chapter One: Motivation for Heavy-Tailed Models; 1.1 Structure of the Book; 1.2 Dominance of the Heaviest Tail Risks; 1.3 Empirical Analysis Justifying Heavy-Tailed Loss Models in OpRisk; 1.4 Motivating Parametric, Spliced and Non-Parametric Severity Models; 1.5 Creating Flexible Heavy-Tailed Models via Splicing; Chapter Two: Fundamentals of Extreme Value Theory for OpRisk; 2.1 Introduction; 2.2 Historical Perspective on EVT and Risk.
2.3 Theoretical Properties of Univariate EVT-Block Maxima and the GEV Family2.4 Generalized Extreme Value Loss Distributional Approach (GEV-LDA); 2.5 Theoretical Properties of Univariate EVT-Threshold Exceedances; 2.6 Estimation Under the Peaks Over Threshold Approach via the Generalized Pareto Distribution; Chapter Three: Heavy-Tailed Model Class Characterizations for LDA; 3.1 Landau Notations for OpRisk Asymptotics: Big and Little 'Oh'; 3.2 Introduction to the Sub-Exponential Family of Heavy-Tailed Models; 3.3 Introduction to the Regular and Slow Variation Families of Heavy-Tailed Models.
Chapter Six: Families of Closed-Form Single Risk LDA Models6.1 Motivating the Consideration of Closed-Form Models in LDA Frameworks; 6.2 Formal Characterization of Closed-Form LDA Models: Convolutional Semi-Groups and Doubly Infinitely Divisible Processes; 6.3 Practical Closed-Form Characterization of Families of LDA Models for Light-Tailed Severities; 6.4 Sub-Exponential Families of LDA Models; Chapter Seven: Single Risk Closed-Form Approximations of Asymptotic Tail Behaviour; 7.1 Tail Asymptotics for Partial Sums and Heavy-Tailed Severity Models.
Gareth W. Peters, Department of Statistical Science, University College of London, London, United Kingdom, Pavel V. Shevchenko., Division of Computational Informatics, the Commonwealth Scientific and Industrial Research Organization, Sydney, Australia.
Includes bibliographical references and index.
Hoboken, New Jersey : John Wiley & Sons, Inc., 2014