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Advances in heavy tailed risk modeling : a handbook of operational risk /
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
2014.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | EBOOKCENTRAL_ocn883305082 | ||
| 003 | OCoLC | ||
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| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 140708s2014 nju ob 001 0 eng | ||
| 010 | |a 2014026795 | ||
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| 020 | |z 9781118909539 |q (hardback) | ||
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| 049 | |a UAMI | ||
| 100 | 1 | |a Peters, Gareth W., |d 1978- | |
| 245 | 1 | 0 | |a Advances in heavy tailed risk modeling : |b a handbook of operational risk / |c 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. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c 2014. | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 520 | |a "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"-- |c Provided by publisher. | ||
| 520 | |a "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"-- |c Provided by publisher. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |6 880-01 |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |6 880-02 |a 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. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Risk management. | |
| 650 | 0 | |a Operational risk. | |
| 650 | 2 | |a Risk Management | |
| 650 | 6 | |a Gestion du risque. | |
| 650 | 6 | |a Risque opérationnel. | |
| 650 | 7 | |a risk management. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Industrial Engineering. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Banks & Banking. |2 bisacsh | |
| 650 | 7 | |a Operational risk |2 fast | |
| 650 | 7 | |a Risk management |2 fast | |
| 700 | 1 | |a Shevchenko, Pavel V. | |
| 758 | |i has work: |a Advances in heavy tailed risk modeling (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGyfvBKDkB8pBvvFyxrqry |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Peters, Gareth W., 1978- |t Advances in heavy tailed risk modeling. |d Hoboken, New Jersey : John Wiley & Sons, Inc., 2014 |z 9781118909539 |w (DLC) 2014015418 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1895736 |z Texto completo |
| 880 | 8 | |6 505-01/(S |a 3.4 Alternative Classifications of Heavy-Tailed Models and Tail Variation3.5 Extended Regular Variation and Matuszewska Indices for Heavy-Tailed Models; Chapter Four: Flexible Heavy-Tailed Severity Models: α-Stable Family; 4.1 Infinitely Divisible and Self-Decomposable Loss Random Variables; 4.2 Characterizing Heavy-Tailed α-Stable Severity Models; 4.3 Deriving the Properties and Characterizations of the α-Stable Severity Models; 4.4 Popular Parameterizations of the α-Stable Severity Model Characteristic Functions; 4.5 Density Representations of α-Stable Severity Models. | |
| 880 | 8 | |6 505-02/(S |a 4.6 Distribution Representations of α-Stable Severity Models4.7 Quantile Function Representations and Loss Simulation for α-Stable Severity Models; 4.8 Parameter Estimation in an α-Stable Severity Model; 4.9 Location of the Most Probable Loss Amount for Stable Severity Models; 4.10 Asymptotic Tail Properties of α-Stable Severity Models and Rates of Convergence to Paretian Laws; Chapter Five: Flexible Heavy-Tailed Severity Models: Tempered Stable and Quantile Transforms; 5.1 Tempered and Generalized Tempered Stable Severity Models; 5.2 Quantile Function Heavy-Tailed Severity Models. | |
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