Master Meetings

Have you decided on which Master programme to study? Would you like more information on the contents and teaching methods at USI? Register at our Master Meetings to attend courses.
The various Master Meetings offer you the opportunity to follow lectures together with the current master students. Guided by a USI student, you can visit the campus and make up your mind as to whether the contents correspond to your study ambitions.

Registration is compulsory. Please register online.


MSc in Finance

A31, Red Building


Prof. Antonietta Mira

The course assumes prior knowledge of the following topics:
Probability of an event; Discrete and continuous random variables.
Probability distribution function, density function and cumulative distribution function.
Conditional probability and distribution. Law of total probability, Independence of events, Bayes Theorem. Expectation and variance of a random variable; Some specific random variables (Bernoulli, Binomial, Uniform, Gaussian).
Basic knowledge of the freeware statistical software R Project.

The course aims to deepen notions of descriptive and inferential statistics both from a theoretical and an applied point of view. The students will be able to analyze a given data set. The freeware statistical software R Project will be used.

SI006, Black Building

Finance and Sustainability
Prof. Laurent Frésard

Corporate Finance, Investments

This course is designed to use concepts and tools of economics to introduce students to important questions and trade-offs related to the role of the financial sector in the transition towards a more sustainable economy.

Topics will include, among others, market failures and regulations, externalities, the objective of firms, divestment vs. engagement, measurement and hedging of climate risk, measurement of firms’ sustainability actions, the impact of growing ESG investors on asset demand, prices and risk, the financing of a sustainable economy and clean technologies, finance and inequalities.

A11, Red Building


Corporate Finance
Prof. Laurent Frésard

This course teaches the logic underlying the firm’s financial decisions: investment, financing, and payout policies.

The main themes of the course are:

  • What is corporate finance?
  • The concept of value
  • The discounted cash-flow (DCF) method in theory and practice
  • Capital budgeting decisions
  • Market efficiency and financial policy
  • Financial structure and the Modigliani-Miller theorem
  • Payout policies: dividends and share repurchases.

A12, Red Building

Data Analytics for Finance
Prof. Peter Gruber


Programming in Finance and Economics I, Statistics at master level.
The R programming language (together with a bit of SQL and Linux) will be used for most part of this course. Students are free to use other languages for the assignments.


Tukey (1962) defines Data Analysis to be “Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”
The goal of this course is to provide the students with the tools and thinking framework to accomplish these tasks with the help of the computer along the entire tool chain of financial data science: from obtaining data to organizing and merging it to analyzing and visualizing it.

A31, Red Building

Financial Econometrics
Prof. Loriano Mancini

Basic knowledge of finance principles, statistics, probability and linear algebra.

The aim of this course is to familiarize the student with some of the most popular econometric methods encountered in applied work in finance.

Building on the material acquired in a basic introductory course in econometrics, the aim of this course is to familiarize the student with some of the most popular econometric methods encountered in applied work in finance. After a brief review of the classical linear model, three major topics are considered:

  • Linear Factor Pricing Model
  • Likelihood Methods, with applications to ARCH and GARCH models

Emphasis is placed on the basic understanding of each approach with computer applications on real data.