Finance and insurance

The course deals with the fundamentals and the main statistical topics of statistical inference, useful for the quantitative analysis of real phenomena, and particularly of financial markets. Students will be also introduced to the statistical software R as a tool for applying statistical methodologies to real data as well as a tool for understanding the statistical theory itself. Students who pass the exam will know the main concepts and procedures of statistical inference, concerning specific parameters, distributions and regression models. Students who pass the exam will acquire skills for data analysis: on the base of the methodologies introduced in the course, they will be able to discriminate among different statistical procedure and to choose the most suitable one for the problem under examination. They will be available to study data and to apply data reduction techniques, to investigate the variability of the phenomenon, and to study the relationship with possible explanatory variables (qualitative and/or quantitative) using the statistical software R. Therefore, starting with a real problem, they will be able to determine the best statistical procedure for analyzing the observed data and to obtain results. They will also be able to analyze in a critical way the obtained results, highlighting pros and cons of the adopted procedures. Students’ critical skill is especially encouraged by the discussion of case studies, which start from a real problem, statistically formalize it, and then show the empirical analysis and critically evaluate the results. The analysis of a case study is also required to students: at the end of the course they will have to produce a report concerning all the phases of the study, including a critical discussion of the results. The evaluation of the report will also concern students’ communication skills and their ability to explain what they learned and the results of the quantitative analysis. The deep comprehension of the statistical methodologies dealt with in the course, will allow the student to understand also models not described in the course, evaluating in complete autonomy their pros and cons.