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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3 courses to be chosen between
To be chosen betweenLegend | 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.
Stochastic differential equations (2017/2018)
Teaching code
4S001444
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/06 - PROBABILITY AND STATISTICS
Period
II sem. dal Mar 1, 2018 al Jun 15, 2018.
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.
Some references
• Introduction to Stochastic Calculus Applied to Finance by D. Lamberton and B. Lapeyre
• Diffusions, Markov Processes and Martingales by L. Rogers and D. Williams, vol 2.
• Stochastic Differential Equations and Diffusion Processes by N. Ikeda and S. Watanabe
• Stochastic differential equations, by B. Øksendal.
• Brownian motion and stochastic calculus, by I. Karatzas and S. Shreve.
• Continuous martingales and Brownian motion, by D. Revuz and M. Yor.
• Stochastic integration and differential equations, by P. Protter.
• Probability with martingales, by D. Williams.
Program
Course Plan
I) BACKGROUND: sigma-algebras, filtrations, conditional expectation, martingale property, variations of a function, quadratic variation.
II) RANDOM WALK: random walk, rescaled random walk, martingale property.
III) BROWNIAN MOTION: definition of Brownian motion, function of a Brownian motion, martingale property, exponential martingale, applications in biology and finance, examples and exercises.
IIIa) SHORT INTRODUCTION TO JUMP PROCESSES: motivation, Poisson processes, properties, discrete time case, introduction to Galton Watson model.
III) WIENER INTEGRAL: motivation, case of step function, definition of Wiener integral, properties, law, martingale, quadratic variation, applications in biology and finance, examples and exercises.
IV) STOCHASTIC INTEGRALS: motivation, case of step function, definition of stochastic integral, properties, martingale, quadratic variation, variance, finite variation processes, Ito processes, applications in biology and finance, examples and exercises.
V) ITO CALCULUS: motivation, Itō-Doeblin formula for Brownian motion, Itō-Doeblin formula for function depending on time,Itō-Doeblin formula for Ito processes, applications in biology and finance, examples and exercises.
VI) SDEs: motivations, definition, existence and uniqueness result, applications in biology and finance, examples and exercises.
VII) MULTI-DIMENSIONAL CASE: multi-dim Brownian motion, correlation, multi-dim Ito-formula,
SDE, applications in biology and finance, examples and exercises.
VIII) CHANGE OF PROBABILITY: motivations, Cameron-Martin theorem, Girsanov theorem, representation of martingale theorem, applications in biology and finance, examples and exercises.
IX) FEYNMAN KAC FORMULA: motivation, Feynman Kac formula, link PDE/SDE, Monte-Carlo methods.
X) JUMPS PROCESSES: Levy processes, characterization and properties.
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| I. Karatzas and S. Shreve | Brownian motion and stochastic calculus | ||||
| D. Revuz and M. Yor | Continuous martingales and Brownian motion | ||||
| L. Rogers and D. Williams | Diffusions, Markov Processes and Martingales (Vol 2.) | ||||
| D. Lamberton and B. Lapeyre | Introduction to Stochastic Calculus Applied to Finance | ||||
| D. Williams | Probability with martingales | ||||
| S. E. Shreve | Stochastic Calculus for Finance II: Continuous-Time Models | Springer, New York | 2004 | ||
| B. Øksendal | Stochastic Differential Equations | ||||
| N. Ikeda and S. Watanabe | Stochastic Differential Equations and Diffusion Processes | ||||
| P. Protter | Stochastic integration and differential equations |
Examination Methods
Wrriten exam. An intermediate (mid-term) exam is forecasted. Then, will be a final exam focusing on the
whole course.
