A Multivariate Claim Count Model for Applications in Insurance

Business & Finance, Industries & Professions, Insurance, Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book A Multivariate Claim Count Model for Applications in Insurance by Daniela Anna Selch, Matthias Scherer, Springer International Publishing
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Author: Daniela Anna Selch, Matthias Scherer ISBN: 9783319928685
Publisher: Springer International Publishing Publication: August 31, 2018
Imprint: Springer Language: English
Author: Daniela Anna Selch, Matthias Scherer
ISBN: 9783319928685
Publisher: Springer International Publishing
Publication: August 31, 2018
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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