Study Plan

Study Plan

Your study experience and our training approach

The Computational Finance master has been carefully designed to achieve a perfect balance between complementary skills.

Curriculum Overview

Quantitative methods and AI techniques are bringing to revolutionary changes in Finance and Insurance.

Strong demand for cross-disciplinary skills:

  • solid understanding of finance and risk
  • ability to think, formalize, analyse quantitatively
  • ability to program and implement computational solutions.

Four pillars: economics, mathematics, statistics, computer science.

Economics

A solid knowledge of economics provides the essential basis to understand the mechanisms and the challenges of financial and insurance markets

Mathematics

Probability represents the language needed to describe and model financial risks. A good knowledge of computational mathematics will improve your understanding of numerical methods in finance.

Statistics

Statistics provides the tools needed to extract information from data, in order to measure and manage financial risks.

Computer Science

The practical implementation of models and methods requires good programming skills, which will enable you to devise efficient algorithms for finance.

Training approach

Close integration between theory and practice

Our students will work on real case studies and projects, bridging theory and practice. They will develop the ability to work independently and in groups, managing projects and making decisions.

Laboratory Activities

Several modules include laboratory activities:

  • Fundamentals of Computational Mathematics
  • Fundamentals of Information Systems
  • Regression and Time Series Models
  • Machine Learning for Finance
  • Quantitative Risk Management
  • Stochastic Finance

Python is the main programming language for all laboratory activities.

Professional Seminars

Regular professional seminars held by practitioners from the finance and insurance industries (useful for internships) and also from asset management, energy and consulting firms.

Program Description

The Computational Finance master is organised across five different types of activities.

1 Bridging the gap

During the first semester, you will acquire solid fundamental skills in economics, mathematics, statistics and computer science, also depending on your previous background.

2 Advanced courses

During the second and third semester, you will attend specialized classes covering all relevant aspects of modern finance, such as risk management, asset pricing, AI in finance, actuarial methods.

3 Customization

During the second year, you will customize your curriculum by choosing two elective courses. This will enable you to develop more specialized skills in a domain of your choice.

4 Seminars

Professional seminars, organised by the Director of the master. The seminars have the primary goal of creating a link between students and professionals from leading financial institutions.

5 Thesis / Stage

The master program is completed by a master thesis, which can take the form of an academic research work or an internship project done in collaboration with a partner institution or firm.

First semester: choice of modules

In the first semester of the first year, you are required to pass the exam of three courses chosen among

  1. Principles of Financial Economics (area: economics)
  2. Fundamentals of Information Systems (area: computer science)
  3. Fundamentals of Computational Mathematics (area: mathematics)
  4. Regression and Time Series Models (area: statistics)

The three courses should be chosen by excluding the one corresponding to your bachelor degree area. For example, if you have a degree in economics or finance, you are exempt from the Principles of Financial Economics exam, while the other three exams are mandatory. Likewise, if your degree is in computer science, you are exempt from the Fundamentals of Information Systems exam, while the three other exams are mandatory. This format ensures that all master students will have a solid basis of common knowledge at the end of the first semester. In the same semester, the module Stochastic Methods is mandatory for all students.

Program Structure

Go to timetable
Show courses▶▼
Training activity ECTS Period Year
Fundamentals of computational mathematics 8.0 1 Sem I
Fundamentals of information systems 8.0 1 Sem I
Principles of financial economics 8.0 1 Sem I
Regression and time series models 8.0 1 Sem I
Stochastic methods 6.0 1 Sem I
Econometrics for credit and market risk 9.0 2 Sem I
Machine learning for finance 9.0 2 Sem I
Financial reporting and risk management (i.c.) 2 Sem I
Financial reporting (mod. a) 6.0 2 Sem I
Risk management and compliance (mod. b) 6.0 2 Sem I
Law and data 6.0 1 Sem II
Quantitative risk management 9.0 1 Sem II
Risk and insurance 6.0 1 Sem II
Stochastic finance 9.0 1 Sem II
Suggested elective courses▶▼

…and more choices are also available!