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
| 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. 2022/23 Computational Algebra not activated; a.a. 2023/24 Homological Algebra not activated)1 module between the following 3 modules among the followingLegend | 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) (2022/2023)
Teaching code
4S001114
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/08 - NUMERICAL ANALYSIS
Period
Semester 2 dal Mar 6, 2023 al Jun 16, 2023.
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.
Program
Programme:
Binary Trees
Continuous time models (Geometric Brownian Motion, Black-Scholes, Feynman-Kac)
Estimating the volatility from historical data
Accelerating the back-folding of a tree
Path Dependent Options
Numerical Methods for Advection-Diffusion equations (Euler, Crank-Nicholson, application to the Black-Scholes PDE)
American and Asian Options
Jump Diffusions and the Merton Model
The Fast Gauss Transform and its application to the pricing of Options
Calibration of a model from historical data
Monte Carlo Methods
Numerical methods for SDE
Applications to Finance in Energy markets
Learning assessment procedures
To pass the exam the student must demonstrate the ability to mathematically model problems in finance and to solve them numerically using the methods discussed during the course. To that end the student will be assigned a project that will involve the implementation and study of some numerical methods for a problem in mathematical finance.
