COMPUTATIONAL FINANCE LAB

Academic year
2020/2021 Syllabus of previous years
Official course title
COMPUTATIONAL FINANCE LAB
Course code
EM2082 (AF:303364 AR:168239)
Modality
On campus classes
ECTS credits
6
Degree level
Master's Degree Programme (DM270)
Educational sector code
SECS-S/06
Period
1st Term
Course year
2
Where
VENEZIA
Moodle
Go to Moodle page
This is a free choice (elective) course that can be chosen by all students of the Master Degree Programme Economics and Finance (Economia e Finanza – Economics-QEM, Finance, Economia e Finanza) and aims to provide notions and skills about programming that will give students the ability to apply financial and economic models on real data.
The course is designed primarily to provide the knowledge of the MATLAB language and development environment necessary to enable the students to develop their own financial applications. To this end, the lectures will be carried out using a computer and students will be asked to use their own computer to prepare for the exam at home.
A second objective of this course is to introduce some important numerical techniques that are widely used in computational finance, especially for derivative pricing, for the evaluation of bonds and for portfolio optimization, as well as for the risk management of these assets.
The instructional goal of the course is acquiring the knowledge and competences to understand the MATLAB language and some important numerical techniques. At the end of the course the student will be able to set up and solve problems of computational finance, also writing scripts in the MATLAB language.
In detail:

a) Knowledge and understanding:
a.1) Ability to understand the basic Matlab kind of variables, instructions and constructions.
a.2) Ability to understand a MATLAB script.
a.3) Ability to understand numerical techniques used in finance such as binomial trees, Monte Carlo simulation, solving an equation using a numerical procedure.

b) Ability to apply knowledge and understanding:
b.1) Ability to use the MATLAB GUI.
b.2) Ability to apply numerical techniques to evaluate financial instruments.
b.3) Ability to write a script in the MATLAB language.
b.4) Ability to organize and integrate data and information needed to solve a financial problem.
b.5) Ability to implement algorithms in MATLAB to evaluate financial instruments and solve financial problems.

c) Ability to make judgements:
c.1) Ability to choose a proper numerical method to solve a financial problem.
c.2) Ability to organize and communicate the steps necessary to implement the solution of a financial problem.
Financial mathematics, Derivatives and insurance or Tecnica dei prodotti finanziari e assicurativi are strongly suggested prerequisites.
The contents of these courses will be considered as known.
The contents of the course are:
1. Introduction to MATLAB and Octave with financial applications
2. Financial applications:
- NPV, IRR
- Bonds
- Dynamics of asset prices
- Derivatives pricing
3. Binomial methods for option pricing
4. Monte Carlo methods for option pricing
5. Other financial applications:
- Stock portfolio optimisation
- Risk measures for asset portfolios
MATLAB Primer, The MathWorks, 2020; this manual can be downloaded from the web page https://it.mathworks.com/help/pdf_doc/matlab/learn_matlab.pdf
Cristina Pocci, Giulia Rotundo, Roeland De Kok, Matlab for applications in Economics and Finance, Apogeo Education, Maggioli Editore, 2017; as an alternative: Cristina Pocci , Giulia Rotundo, Roeland De Kok, Matlab Per le applicazioni economiche e finanziarie, Apogeo Education, Maggioli Editore, 2017.
Lectures notes on Monte Carlo simulation for option pricing.

Optional reading (suggested):
Brian R. Hunt, Ronald L. Lipsman, Jonathan M. Rosenberg, A Guide to MATLAB, For Beginners and Experienced Users, Academic Press, Cambridge, 2014, 3rd Edition.
Grading is based on the evaluation of homeworks assigned weekly during the course (60% of the final grade), a project (30% of the final grade) and a final oral exam (10% of the final grade).
The homework assignments aim to assess the ability of the student to solve problems assigned by the teacher using MATLAB.
The project aims to stimulate and assess the problem-solving ability of the student; the student will be asked to tackle a class of problems of computational finance agreed upon with the teacher by applying a proper numerical procedure, writing a MATLAB program that provides a general solution and writing an accompanying short paper that explains in detail the financial or economic problem tackled, the quantitative model adopted and the procedure applied and describes how to use the MATLAB script. The exam will conclude with an oral examination in which the student will discuss the homework assignments and the project and will answer questions about the topics covered in the course.
A frontal course with practical lectures in which financial problems are tackled on real data.
The course also uses educational materials available on the university's e-learning platform moodle.unive.it.
The computer will be used during the lessons both by the teacher and by students, and the teacher will stimulate students to personally write financial software.
Exercises will be assigned to stimulate and test the acquisition of the knowledge and abilities on the topics covered during the week; students are expected to solve them regularly at home.
The attendance of lessons is strongly recommended.
English
Students are required to register in the related course (Computational Finance Lab) web page of the university e-learning platform moodle.unive.it. The e-learning tools of moodle.unive.it will be used throughout the course. The Matlab Grader Online will also be used for grading the homeworks.
Additional information, updates and further material on the course will be provided in the web page of the course in moodle.
oral

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development

Definitive programme.
Last update of the programme: 13/07/2020