Statistical Inference for Copula and Tail Copula Models with Applications to Finance and Insurance

ISBN: 978-1-4987-6865-8
Verlag: Taylor & Francis Inc

Buch, Englisch, 200 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Chapman and Hall/CRC Financial Mathematics Series

This book will cover statistical inference for copula and tail copula models with applications in finance, insurance and risk management. After giving a quick introduction to copula and tail copula models, it will focus on various up-to-date statistical inference procedures, including point and interval estimation and goodness-of- t tests, for both copulas and tail copulas based on either independent data or dependent data. A chapter on applications in nance, insurance and risk management will be provided with R code.

Autoren/Hrsg.

Peng, Liang

Zhang, Zhengjun

Inhaltsverzeichnis

Introduction. Inference for Copula on Independent Data. Inference for Tail Copulas on Independent Data. Inference for Dependent Data. Applications.

Über Autor(innen)

Professor Liang Peng is the Thomas P Bowles Chair professor of Actuarial Science in the Department of Risk Management and Insurance in the Robinson College of Business at Georgia State University, and is the fellow of both Institute of Mathematical Statistics and American Statistical Association. Peng has extensive research experience in extreme value theory, time series analysis, nonparametric statistics, copula models, empirical likelihood methods and uncertainty quanti cation for various risk measures.