A Multivariate Claim Count Model for Applications in Insurance

ISBN-13: 9783319928678 / Angielski / Twarda / 2018 / 158 str.

Daniela Anna Selch; Matthias Scherer

Recenzje

"The monograph is an in-depth work concerning important topics in the actuarial field; it is designed to present a time-dynamic model for multivariate claim counts and its applications in the actuarial framework. ... The monograph represents a reference book for researchers and actuaries." (Emilia Di Lorenzo, zbMATH 1417.91006, 2019)

Spis treści

1 Motivation and Model.- 2 Properties of the Model.- 3 Estimation of the Parameters.- 4 Applications and Extensions.- 5 Appendix: Technical Background.- References.- Index.

O autorze

Daniela Selch currently works as a quantitative analyst for the Equities – Structured Products and Strategies team of Barclays Quantitative Analytics in London. Previously, she was a research assistant at the Chair of Mathematical Finance at the Technical University of Munich, where she earned her PhD for the results summarized in this book. Her PhD thesis was awarded the SCOR-price for actuarial sciences and she presented at several scientific conferences, including the ICBI Global Derivatives Trading & Risk Management 2016, Budapest as invited speaker.

Matthias Scherer is Professor for Financial Mathematics at the Technical University of Munich, member of the board of the German Society for Insurance and Financial Mathematics (DGVFM), and associate editor of the journals Dependence Modelling and RISIKO MANAGER. He has (co-)authored scientific papers in the areas finance and actuarial science, multivariate statistics, probability theory, and quantitative risk management. 

Blurb

This monograph presents a time-dynamic model for multivariate claim counts in actuarial applications.

Inspired by real-world claim arrivals, the model balances interesting stylized facts (such as dependence across the components, over-dispersion and the clustering of claims) with a high level of mathematical tractability (including estimation, sampling and convergence results for large portfolios) and can thus be applied in various contexts (such as risk management and pricing of (re-)insurance contracts). The authors provide a detailed analysis of the proposed probabilistic model, discussing its relation to the existing literature, its statistical properties, different estimation strategies as well as possible applications and extensions.

Actuaries and researchers working in risk management and premium pricing will find this book particularly interesting. Graduate-level probability theory, stochastic analysis and statistics are required.