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Bayesian Claims Reserving Methods in Non-life Insurance with Stan

An Introduction

  • Book
  • © 2018

Overview

Authors:
  1. Guangyuan Gao

    1. School of Statistics, Renmin University of China, Beijing, China

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  • The first book provides explicit Stan code for non-life claims reserving

  • The book has a thorough review of many aspects of Bayesian statistics, and relates them to claims reserving problem

  • The book addresses three important points in claims reserving: proposing a stochastic payments per claim incurred model (Section 4), estimating the tail factor via basis expansion models (Section 5), and aggregating claims liabilities by copulas (Section 6)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xii
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  2. Introduction

    • Guangyuan Gao
    Pages 1-8

  3. Bayesian Fundamentals

    • Guangyuan Gao
    Pages 9-33

  4. Advanced Bayesian Computation

    • Guangyuan Gao
    Pages 35-71

  5. Bayesian Chain Ladder Models

    • Guangyuan Gao
    Pages 73-115

  6. Bayesian Basis Expansion Models

    • Guangyuan Gao
    Pages 117-152

  7. Multivariate Modelling Using Copulas

    • Guangyuan Gao
    Pages 153-183

  8. Epilogue

    • Guangyuan Gao
    Pages 185-190

  9. Back Matter

    Pages 191-205
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Keywords

About this book

This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on Stan, a specialized software environment which applies Hamiltonian Monte Carlo method and variational Bayes.  

Authors and Affiliations

  • School of Statistics, Renmin University of China, Beijing, China

    Guangyuan Gao

About the author

Guangyuan Gao, lecturer in actuarial science, School of Statistics at the Renmin University of China.


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