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.
Type D and Type F activities
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 2025/2026| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Algorithms | D |
Roberto Segala
(Coordinator)
|
| 1° 2° | Scientific knowledge and active learning strategies | F |
Francesca Monti
(Coordinator)
|
| 1° 2° | Genetics | D |
Massimo Delledonne
(Coordinator)
|
| 1° 2° | History and Didactics of Geology | D |
Guido Gonzato
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Advanced topics in financial engineering | F |
Luca Di Persio
(Coordinator)
|
| 1° 2° | Algorithms | D |
Roberto Segala
(Coordinator)
|
| 1° 2° | Python programming language | D |
Vittoria Cozza
(Coordinator)
|
| 1° 2° | Organization Studies | D |
Giuseppe Favretto
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | ECMI modelling week | F | Not yet assigned |
| 1° 2° | ESA Summer of code in space (SOCIS) | F | Not yet assigned |
| 1° 2° | Google summer of code (GSOC) | F | Not yet assigned |
| 1° 2° | Introduzione all'analisi non standard | F |
Sisto Baldo
|
| 1° 2° | C Programming Language | D |
Pietro Sala
(Coordinator)
|
| 1° 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 1° 2° | Mathematics mini courses | F |
Marco Caliari
(Coordinator)
|
Stochastic Calculus (2020/2021)
Teaching code
4S008268
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/06 - PROBABILITY AND STATISTICS
Period
II semestre dal Mar 1, 2021 al Jun 11, 2021.
Learning outcomes
This course will provide an introduction to the theory of Stochastic Differential Equations (SDEs), mainly based on the Brownian motion type of noise. The purpose of this course is to introduce and analyse probability models that capture the stochastic features of the system under study to predict the short and long term effects that this randomness will have on the systems under consideration. The study of probability models for continuous-time stochastic processes involves a broad range of mathematical and computational tools. This course will strike a balance between the mathematics and the applications. The main applications will be mathematical finance, biology and populations evolution, also with respect to their descriptions in terms of the associated SDEs. Topics include: construction of Brownian motion; martingales in continuous time; stochastic integral; Ito calculus; stochastic differential equations; Girsanov theorem; martingale representation; the Feynman-Kac formula and Lévy processes.
Program
* Probability essential recalls
* SP: definitions/main properties recall ; Martingales ; Option Sampling Theorem ; Quadratic Variation ;
* Stochastic processes at discrete time: recalls and emphasis on random walk (starting from the binomial model, also in more than 1 dimension);
* Different constructions of the Brownian motion: Kolmogorov Consistency Theorem / Kolmogorov-
Cénstor Th.eorem;
* Properties of the Brownian motion
* Derivation/construction of the Stochastic Integral(s) notion(s)
* Ito-Doeoblin rule: Levy's Criteria / Martingale Representation
* Stratonovich approach / Ito representation Theorem (applications/examples)
* Markov processes and relation(s) with the Brownian motion sp [further Bm's properties]
* Girsanov formula / Cameron-Martin (Girsanov) Theorem and Exponential Martingales
* Construction and rigorous derivation of Stochastic Differential Equations
* Strong solutions / Gronwall Lemma / Weak solutions (for SDEs)
* Diffusions / Semi-group approach / Markov property(ies)
* Dynkin's formula / Kolmogorov equation(s) / Feynman-Kac theorem
* Interplay between PDEs and SPDEs (via F-K theorem)
* SDEs application w.r.t. the Financial framework
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| I. Karatzas and S. Shreve | Brownian motion and stochastic calculus | ||||
| L. Rogers and D. Williams | Diffusions, Markov Processes and Martingales (Vol 2.) | ||||
| Hoel, P. G., Port, S. C. and Stone, C. J. | Introduction to Stochastic Processes | Houghton Mifflin, Boston | 1972 | ||
| S. E. Shreve | Stochastic Calculus for Finance II: Continuous-Time Models | Springer, New York | 2004 | ||
| B. Øksendal | Stochastic Differential Equations | ||||
| P. Protter | Stochastic integration and differential equations |
Examination Methods
Oral exam with written exercise:
the exam is based on open questions and/or on the presentation of a project agreed with the course professor and or on the resolution of written exercises to be solved during the test itself. Questions, open-ended and exercises, aim at verify both the knowledge about arguments developed within the course, the solution of concrete problems belonging to Mathematical Finance, and to the acquired acquaintance of associated tools of stochastic analysis.
