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Topics in life and disability insurancePrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2015 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Stockholm: KTH Royal Institute of Technology, 2015. , 21 p.
##### Series

TRITA-MAT-A, 2015:09
##### National Category

Probability Theory and Statistics
##### Research subject

Applied and Computational Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-175334ISBN: 978-91-7595-701-2OAI: oai:DiVA.org:kth-175334DiVA: diva2:860294
##### Public defence

2015-11-06, F3, Lindstedtsvägen 26, Kungliga Tekniska högskolan, Stockholm, 13:15 (English)
##### Opponent

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##### Supervisors

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#####

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##### Note

##### List of papers

This thesis consists of five papers, presented in Chapters A-E, on topics in life and disability insurance. It is naturally divided into two parts, where papers A and B discuss disability rates estimation based on historical claims data, and papers C-E discuss claims reserving, risk management and insurer solvency.In Paper A, disability inception and recovery probabilities are modelled in a generalized linear models (GLM) framework. For prediction of future disability rates, it is customary to combine GLMs with time series forecasting techniques into a two-step method involving parameter estimation from historical data and subsequent calibration of a time series model. This approach may in fact lead to both conceptual and numerical problems since any time trend components of the model are incoherently treated as both model parameters and realizations of a stochastic process. In Paper B, we suggest that this general two-step approach can be improved in the following way: First, we assume a stochastic process form for the time trend component. The corresponding transition densities are then incorporated into the likelihood, and the model parameters are estimated using the Expectation-Maximization algorithm.In Papers C and D, we consider a large portfolio of life or disability annuity policies. The policies are assumed to be independent conditional on an external stochastic process representing the economic-demographic environment. Using the Conditional Law of Large Numbers (CLLN), we establish the connection between claims reserving and risk aggregation for large portfolios. Moreover, we show how statistical multi-factor intensity models can be approximated by one-factor models, which allows for computing reserves and capital requirements efficiently. Paper C focuses on claims reserving and ultimate risk, whereas the focus of Paper D is on the one-year risks associated with the Solvency II directive.In Paper E, we consider claims reserving for life insurance policies with reserve-dependent payments driven by multi-state Markov chains. The associated prospective reserve is formulated as a recursive utility function using the framework of backward stochastic differential equations (BSDE). We show that the prospective reserve satisfies a nonlinear Thiele equation for Markovian BSDEs when the driver is a deterministic function of the reserve and the underlying Markov chain. Aggregation of prospective reserves for large and homogeneous insurance portfolios is considered through mean-field approximations. We show that the corresponding prospective reserve satisfies a BSDE of mean-field type and derive the associated nonlinear Thiele equation.

QC 20151012

Available from: 2015-10-12 Created: 2015-10-12 Last updated: 2015-10-13Bibliographically approved1. Stochastic modelling of disability insurance in a multi-period framework$(function(){PrimeFaces.cw("OverlayPanel","overlay675636",{id:"formSmash:j_idt423:0:j_idt427",widgetVar:"overlay675636",target:"formSmash:j_idt423:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. A hidden Markov approach to disability insurance$(function(){PrimeFaces.cw("OverlayPanel","overlay860304",{id:"formSmash:j_idt423:1:j_idt427",widgetVar:"overlay860304",target:"formSmash:j_idt423:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Risk aggregation and stochastic claims reserving in disability insurance$(function(){PrimeFaces.cw("OverlayPanel","overlay675643",{id:"formSmash:j_idt423:2:j_idt427",widgetVar:"overlay675643",target:"formSmash:j_idt423:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Aggregation of one-year risks in life and disability insurance$(function(){PrimeFaces.cw("OverlayPanel","overlay860325",{id:"formSmash:j_idt423:3:j_idt427",widgetVar:"overlay860325",target:"formSmash:j_idt423:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. Nonlinear reserving in life insurance: aggregation and mean-field approximation$(function(){PrimeFaces.cw("OverlayPanel","overlay860328",{id:"formSmash:j_idt423:4:j_idt427",widgetVar:"overlay860328",target:"formSmash:j_idt423:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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