KNOWLEDGE AND UNDERSTANDING. This course aims at providing students with the understanding of some statistical tools and techniques that are relevant for today’s Insurance Industry and in the financial framework. On completion of the course, the students will be able to explain core concepts and methods in data science related to, e.g., risk assessment, time series analysis and forecasting. APPLYING KNOWLEDGE AND UNDERSTANDING. The course will provide students with practical tools for data analysis and the selection of the appropriate statistical model for dealing with various problems, with specific application in the insurance context. On completion of the course, the students will be able to implement the appropriate methods and models in practical problems, using R to analyze, visualize and estimate relevant quantities from data in the insurance and financial context. MAKING JUDGMENTS. By the end of the course, students will be able to propose their own strategies to deal with the statistical analysis of a data set by proper (multivariate) techniques and articulate conclusions on the results from the estimated models. COMMUNICATION SKILLS. By the end of the course, students will empower their ability to describe statistical concepts for an effective presentation and discussion of project results. LEARNING SKILLS. By the end of the course, students will have developed critical thinking abilities, which are essential to the understanding of more advanced statistical analyses.

Knowledge of the basics of statistical methods and tools is strongly recommended. Moreover, a basic knowledge of spreadsheet and database is recommended.

The course aims to provide students with the knowledge of some statistical issues that are relevant in the insurance field, with a focus on risk assessment and the tools of computational statistics in insurance practice The course is structured into two parts, which address some specific topics related to the analysis of data in the insurance and finance contexts: risk measures and multivariate modelling in finance and insurance; copula functions and their use for modelling dependence among risk factors. the use of statistical models, e.g., spatial linear regression, to address estimation and forecasting issues in the insurance practice. Each part will discuss both theoretical and applied concepts. The course will consider several examples using both synthetic and real data to illustrate the implementation of the methods and techniques presented via the “R” statistical software for data analysis and visualization.

- J. McNeil, R. Frey, and P. Embrechts, “Quantitative Risk Management: Concepts, Techniques and Tools’’ (2015), Revised Edition, Princeton Series in Finance. Different resources (links, articles, movies) will be suggested after every session to students who wish to deepen the topics of the course; many articles are shared during the course. Additional material (e.g., slides, exercises) will be available at the Moodle page of the course.

The course consists of frontal lectures and computer labs. Home assignments will be scheduled during the course. Labs will focus on the use of the R software. Students will be encouraged to actively interact in the classroom, by asking questions and offering comments pertaining to the course.

Should it be necessary to make any changes to this syllabus in compliance with safety protocols related to the COVID19 emergency or for other reasons, an announcement will be posted on the Department and course websites.

The final exam consists of two parts: 1. A written test of 1 hour with quizzes and general questions concerning the topics of the course. 2. A project to be developed with R presenting the statistical analysis of a dataset and the implementation of some of the techniques discussed in class. The final grade ranges from 18/30 to 30/30 cum laude and will be the average of the evaluations resulting from the written test and the project. It is compulsory to register to the exam on esse3 within the given deadline.

Questo insegnamento approfondisce argomenti strettamente connessi a uno o più obiettivi dell’Agenda 2030 per lo Sviluppo Sostenibile delle Nazioni Unite