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Modélisation de la dépendance temporelle des sinistres en assurance non vie et enjeux de l’évaluation du Passif

Abstract : In this thesis a different aspects of dependence modeling are considered. Indeed, temporal dependence structures between claims amounts and between lines of business are analyzed. In the first chapter, a general introduction on modeling dependence in insurance is provided. The second chapter is essentially constituted by the article "Modeling Temporal Dependence of Claims In Insurance Using Autoregressive Conditional Amount Models", written with Christian de Peretti and Lotfi Belkacem, (see Araichi et al. (2013)) It deals with the problem of existing a temporal dependence structure between claims amounts of one line of business. To this end, we propose a new model for handling the dynamic behaviour of claims amounts in insurance companies using an Autoregressive Conditional Amount (ACA) framework. This model will be named Autoregressive Conditional Amount Model (ACA). A Gamma ACA model and a Generalized Extreme Value ACA model are proposed. It is shown that these models are more appropriate to describe and to forecast the process of claims of the lines Auto Damage and Auto Liability than traditional models. Furthermore, a parametric Value at Risk based on ACA framework (VaR ACA) is proposed for evaluating a coverage amount of these claims. Using backtesting techniques, the VaR ACA provides an accurate estimation of risk. The third chapter of this thesis is based on the article "Generalized Autoregressive Conditional Sinistrality Model: A novel model for claims reserving in Non life insurance", written with Christian de Peretti and Lotfi Belkacem, (see Araichi et al. (2015)). In this chapter, a Generalized Autoregressive Conditional Sinistrality Model (GACSM) for claims is proposed. We extend the Generalized Linear Model (GLM) by incorporating temporal dependence between claims amounts of one triangle. The GACSM is used for model parameter estimation and consistency of such estimate is proved. Bootstrap procedure is implemented for prediction reserves and prediction errors. Results show that taking into account the temporal dependence between losses improves the precision of the reserve distribution estimate, and thus evaluates an accurate SCR. Finally the fourth chapter is based on the article "Time Varying Copula Model for claims reserving in Non life insurance", written with Christian de Peretti and LotfiBelkacem. In this chapter, a time varying copula models to understand the behavior of claims amounts of two lines of business. Time varying copula functions with a Generalized Autoregressive Conditional Sinistrality model are used to analyze the evolution in time of dependence between two lines and the temporal dependence between claims of each line. Simulation study is performed to highlight the impact on reserves and Solvency Capital Requirement. Results show that our approach provides a diversification effect between claims amounts
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Sawssen Araichi. Modélisation de la dépendance temporelle des sinistres en assurance non vie et enjeux de l’évaluation du Passif. Gestion et management. Université Claude Bernard - Lyon I, 2015. Français. ⟨NNT : 2015LYO10170⟩. ⟨tel-01369002⟩

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