Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Mathematics - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year It will be activated in the A.Y. 2025/2026
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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1 module between the following:
- A.A. 2024/2025 Computational algebra not activated;
- A.A. 2025/2026 Homological Algebra not activated.1 module between the following 3 modules among the following
- A.A. 2025/2026 Homological algebra not activated.Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Numerical methods for mathematical finance (seminar course) (2024/2025)
Teaching code
4S001114
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/08 - NUMERICAL ANALYSIS
Period
Semester 1 dal Oct 1, 2024 al Jan 31, 2025.
Courses Single
Authorized
Learning objectives
The course will discuss various numerical methods for the pricing of the main financial instruments. An emphasis will be made on finance in the Energy industry. At the end of the course the student is expected to have the ability to construct and develop mathematical models for the stochastic processes of finance, to be able to analyze their limits and applicability and to solve them numerically.
Prerequisites and basic notions
Basic tools of applied probability.
Basic knowledge of the theory of stochastic processes.
Program
The following lessons are scheduled in classroom L:
Tuesday 5/11 16.30-18.30
- Introduction
- Simulations of Brownian Motion and Geometric Brownian Motion
Tuesday 12/11 16.30-18.30
- Simulation of SDEs via Euler-Maruyama and Millstein methods
- Heston and CIR model
Tuesday 19/11 16.30-18.30
- Option Pricing
- MonteCarlo method
Tuesday 26/11 16.30-18.30
- Introduction to energy markets
- Reinforcement Learning application to day-ahead electricity market
- Projects assignment
Bibliography
Didactic methods
The lessons (see timetable) will be held in the classroom L
Learning assessment procedures
In the last lesson, projects will be provided to be developed individually (or in small groups) by the students.
Evaluation criteria
The course evaluation will focus on active participation in lectures, theoretical understanding, individual project development, and collaboration with the instructor. Key aspects include the ability to apply theoretical concepts, originality and independence in the project, quality of documentation and final presentation, as well as effective use of received feedback.
Criteria for the composition of the final grade
The mark is based on participation in classroom seminars, active participation in the entire course, and completion of the final project to be discussed in a seminar held by the student.
Exam language
English
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