ID:
162002-ENG
Dettaglio:
SSD: Statistica
Durata: 72
CFU: 9
Sede:
BERGAMO
Url:
ECONOMICS AND FINANCE - 162-R-EN/Quantitative Finance and Insurance Anno: 1
Anno:
2026
The course aims at strengthening the knowledge of statistics and probability obtained during the bachelor, and at teaching how to apply statistical methods in the economic and financial fields.
At the end of the course, the students will gain the ability to:
a) choose, apply and test appropriate statistical methods and models for the analysis of different types of economic and financial data;
b) use the free open-source statistical software R (http://www.r-project.org) for statistical data analysis, as well as for modeling and forecasting economic and financial time series;
c) interpret the results in a decision-making perspective.
The course will consist in class lectures and R lab sessions. The latter will take approximately two hours per week, and will be scheduled within the course hours. Attending class lectures and R labs is strongly recommended.
The exam will be the same for both attending and non-attending students, and will consist in:
- multiple-choice questions about theoretical topics or short applications of the studied methods;
- exercises to be solved using the R software, aimed at evaluating the ability of the student in analyzing financial data and interpreting statistical outputs.
These two parts of the exam are each worth 50% of the total score, approximately.
The instructor reserves the right to require an additional oral examination, even after the written examination has taken place, whenever this is considered necessary to verify the effective acquisition of the knowledge and skills specified in the course learning objectives and to ensure the appropriate assignment of the final grade.
- Financial variables: returns and their distributional properties.
- Review of the main statistical concepts necessary for economic and financial data analysis (e.g. random variables, probability distributions, hypothesis testing, etc.);
- Statistical methods for univariate data (histogram, QQ-plot and normal probability plot, data transformation, distribution parameters, skewness and kurtosis indexes, tests of normality, heavy-tailed distributions) and multivariate data (covariance matrix, multivariate Normal, linear combinations of random variables);
- Multiple linear regression: model estimation, ANOVA, model evaluation and selection, check of model assumptions;
- Stochastic processes and time series: AR, MA, ARMA and ARIMA models (definition, properties, estimation, and forecasting).