COMPUTATIONAL FINANCE LAB
- Academic year
- 2019/2020 Syllabus of previous years
- Official course title
- COMPUTATIONAL FINANCE LAB
- Course code
- EM2082 (AF:278982 AR:159992)
- 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
Contribution of the course to the overall degree programme goals
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.
Expected learning outcomes
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.
Pre-requirements
The contents of these courses will be considered as known.
Contents
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
Referral texts
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.
Assessment methods
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 a short paper that explains 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.
Teaching methods
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.