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87279 - NON-LIFE INSURANCE

Anno Accademico 2022/2023

                
                        Docente:
                        Alexandru-Valentin Asimit
                    
                        Crediti formativi:
                        6
                    
                        SSD:
                        SECS-S/06
                    
                        Lingua di insegnamento:
                        Inglese
                    
                        Modalità didattica:
                        Convenzionale - Lezioni in presenza
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Laurea Magistrale in
                            Quantitative Finance (cod. 8854)

                                    Orario delle lezioni
                                
dal 28/11/2022 al 03/12/2022

Conoscenze e abilità da conseguire

At the end of the course the student is familiar with the fundamental statistical tools needed to study and manage non-life insurance risks. He is able to compute premia and reserves of non-life insurance contracts.

Contenuti

This module aims to explain the fundamental risk modelling for insurance applications. Specifically, statistical and probabilistic modelling for non-life insurance risks are discussed as follow:

Loss distributions
Reinsurance
Risk models
Dependence and measures of associations
Extreme Value Theory

On successful completion of this module, you will be expected to be able to apply insurance risk models accurately and evaluate their use, explain how the dependence models and analysis of extreme/rare events work in practice, etc.

Testi/Bibliografia

Beirlant, J., Goegebeur, Y., Teugels, J. and Segers, J. (2005) Statistics of Extremes: Theory and Applications, Chichester, Wiley Series in Probability and Statistics.

Kaas, R., Goovaerts, M., Dhaene, J. and Denuit, M. (2008) Modern Actuarial Risk Theory, 2^nd Edition, Dordrecht, Springer.

Klugman, S.A. Panjer, H.H. and Willmot, G.E. (2008) Loss Models: from data to decisions, 3^rd Edition, Hoboken, John Wiley & Sons.

Metodi didattici

There are 30 lecture hours that combine mathematical derivations, practicing exercise and run data analysis computer labs in R.

Modalità di verifica e valutazione dell'apprendimento

This module is assessed by a 90 minutes written examination.

Strumenti a supporto della didattica

Lectures are delivered via slides and R based labs. Written notes are provided for all lectures and problems sets that will prepare you for the final exams. All students should be able to have a laptop that is able to run R scripts. R studio is a simpler way to run R scripts, but you could download the standard R free software from https://cran.r-project.org/bin/windows/base/.

Orario di ricevimento

Consulta il sito web di Alexandru-Valentin Asimit