Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

At the end of the course the student masters the main concepts of numerical methods applied in financial mathematic. The course will cover the Montecarlo method and the empirical calibration of stochastic processes along with the writing of codes in MatLab.

Course contents

1. Financial Toolbox: work with matrices, from string to a serial number, plot financial data, present value and annuities, fixed income bond: pricing, yields and sensitivities, Derivatives: Black-Scholes and Binomial model, Portfolio analysis: efficient frontier, optimal portfolio performance.

2. MatLab for Financial Applications: work with real data, MatLab variables and graphical representation, M-files: script versus function, Statistics and data analysis, Programming: functions' structure, vectorization and evaluation of the performance.

3. Programming on special topics: Classic financial math, Bond market and term strictures, Asset management, Derivatives: pricing on stock and fixed income markets.


  • MatLab for Financial Applications, The MathWorks Training Services, 2006
  • Financial Toolbox for use with MatLab, User's Guide-version 3, The MathWorks 2006

Teaching methods

Theoretical lessons will be support by team's works based on examples discussed in class and aimed to implement specific applications. The main feature of the course is a strongly applied nature aimed to get the students used to work with market data.  

Assessment methods

The students will be requested to work on a individual/team project assigned by the teacher. The delivery must be within the deadline.

Teaching tools

Teaching tools will be blackboard and slides.

Office hours

See the website of Silvia Romagnoli