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87279 - Non-Life Insurance

Academic Year 2022/2023

                
                        Docente:
                        Alexandru-Valentin Asimit
                    
                        Credits:
                        6
                    
                        SSD:
                        SECS-S/06
                    
                        Language:
                        English
                    
                        Teaching Mode:
                        Traditional lectures
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Quantitative Finance (cod. 8854)

                                    Course Timetable
                                
from Nov 28, 2022 to Dec 03, 2022

Learning outcomes

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.

Course contents

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.

Readings/Bibliography

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.

Teaching methods

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

Assessment methods

This module is assessed by a 90 minutes written examination.

Teaching tools

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/.

Office hours

See the website of Alexandru-Valentin Asimit