Fundamental Aspects of Operational Risk and Insurance Analytics and Advances in Heavy Tailed Risk Modeling: Handbooks of Operational Risk Set

Fundamental Aspects of Operational Risk and Insurance Analytics and Advances in Heavy Tailed Risk Modeling: Handbooks of Operational Risk Set
Author :
Publisher : Wiley
Total Pages : 0
Release :
ISBN-10 : 1118909577
ISBN-13 : 9781118909577
Rating : 4/5 (77 Downloads)

Book Synopsis Fundamental Aspects of Operational Risk and Insurance Analytics and Advances in Heavy Tailed Risk Modeling: Handbooks of Operational Risk Set by : Marcelo G. Cruz

Download or read book Fundamental Aspects of Operational Risk and Insurance Analytics and Advances in Heavy Tailed Risk Modeling: Handbooks of Operational Risk Set written by Marcelo G. Cruz and published by Wiley. This book was released on 2015-07-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two cutting-edge guides for the theories, applications, and statistical methodologies essential to operational risk and heavy tailed risk modeling Focusing on the quantitative aspects of heavy tailed loss processes in operational risk and relevant insurance analytics, Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk presents comprehensive coverage of the latest research on the theories and applications in risk measurement and modeling techniques. Featuring a unique balance of mathematical and statistical perspectives, the handbook begins by introducing the motivation for heavy tailed risk processes in high consequence low frequency loss modeling. With a companion, Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk, the book provides a complete framework for all aspects of operational risk management. Fundamental Aspects of Operational Risk and Insurance Analytics covers the theories, applications, and models inherent in any discussion of the fundamentals of operational risk, with a primary focus on Basel II/III regulation, modeling dependence, estimation of risk models, and modeling the data elements.


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