The Art of Finding Hidden Risks: Hidden Regular Variation in the 21st Century (Springer Series in Operations Research and Financial Engineering) - Hardcover

Resnick, Sidney

Hardcover

ISBN 10: 3031575989 ISBN 13: 9783031575983

Publisher: Springer, 2024

Synopsis

This text gives a comprehensive, largely self-contained treatment of multivariate heavy tail analysis. Emphasizing regular variation of measures means theory can be presented systematically and without regard to dimension. Tools are developed that allow a flexible definition of "extreme" in higher dimensions and permit different heavy tails to coexist on the same state space leading to "hidden regular variation" and "steroidal regular variation". This emphasizes when estimating risks, it is important to choose the appropriate heavy tail. Theoretical foundations lead naturally to statistical techniques; examples are drawn from risk estimation, finance, climatology and network analysis. Treatments target a broad audience in insurance, finance, data analysis, network science and probability modeling. The prerequisites are modest knowledge of analysis and familiarity with the definition of a measure; regular variation of functions is reviewed but is not a focal point.

About this title

About the Author

Sidney Resnick is the Lee Teng-Hui Professor in Engineering Emeritus in Cornell University's School of Operations Research and Information Engineering in Ithaca NY. He joined Cornell after posts at Technion, Stanford and Colorado State University. He has served on numerous editorial boards, had numerous visiting appointments and, to date, has published 4 previous books and co-authored 195 research papers. From 1998--2003, Resnick was Director of the School of ORIE.

From the Back Cover

This text gives a comprehensive, largely self-contained treatment of multivariate heavy tail analysis. Emphasizing regular variation of measures means theory can be presented systematically and without regard to dimension. Tools are developed that allow a flexible definition of "extreme" in higher dimensions and permit different heavy tails to coexist on the same state space leading to "hidden regular variation" and "steroidal regular variation". This emphasizes when estimating risks, it is important to choose the appropriate heavy tail. Theoretical foundations lead naturally to statistical techniques; examples are drawn from risk estimation, finance, climatology and network analysis. Treatments target a broad audience in insurance, finance, data analysis, network science and probability modeling. The prerequisites are modest knowledge of analysis and familiarity with the definition of a measure; regular variation of functions is reviewed but is not a focal point.

Edition details

• Publisher: Springer
• Publication date: 2024
• Language: English
• ISBN 10: 3031575989
• ISBN 13: 9783031575983
• Binding: Hardcover
• Number of pages: 275