Advanced Topics in Stochastics Calculus
A.Y. 2021/2022
Learning objectives
The goal of the course is to explore stochastic calculus in depth, by extending the study of stochastic integration from Brownian motion to continuous local martingales. Moreover, based on such an extension, we will turn to other important topics such as: generalised Girsanov theorem, local times and Tanaka's formula as an extension of Ito's formula, weak solutions of stochastic differential equations and Stroock-Varadhan martingale problem, propagation of chaos for mean-field particle systems.
Expected learning outcomes
Detailed knowledge of stochastic calculus for continuous semimartingales (even in a non-regular context) with an introduction to limit theorems for mean-field particle systems.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
More specific information on the delivery modes of training activities for academic year 2021/22 will be provided over the coming months, based on the evolution of the public health situation
Course syllabus
Part I. Review of continuous martingale theory and finite variation processes.
Part II. Stochastic integral with respect to continuous semimartingales, Ito's formula, Dumbis-Dubins-Schwarz theorem on time-changes, generalized Girsanov theorem.
Part III. Ito-Tanaka-Meyer formula, local times of continuous semimartingales, occupation density formula.
Part IV. Weak solutions of stochastic differential equations (SDE), Stroock-Varadhan martingale problem.
Part V. Introduction to mean-field models: nonlinear SDE of McKean-Vlasov type, propagation of chaos.
Part VI. (If time allows) Introduction to large deviations theory.
Part II. Stochastic integral with respect to continuous semimartingales, Ito's formula, Dumbis-Dubins-Schwarz theorem on time-changes, generalized Girsanov theorem.
Part III. Ito-Tanaka-Meyer formula, local times of continuous semimartingales, occupation density formula.
Part IV. Weak solutions of stochastic differential equations (SDE), Stroock-Varadhan martingale problem.
Part V. Introduction to mean-field models: nonlinear SDE of McKean-Vlasov type, propagation of chaos.
Part VI. (If time allows) Introduction to large deviations theory.
Prerequisites for admission
1) Advanced notions on probability.
2) Stochastic integral with respect to Brownian motion.
3) Stochastic differential equations (strong solutions).
2) Stochastic integral with respect to Brownian motion.
3) Stochastic differential equations (strong solutions).
Teaching methods
Taught lectures
Teaching Resources
1) O. Kallenberg: "Foundations of Modern Probability", Springer, 2002.
2) I. Karatzas, S. Shreve: "Brownian motion and stochastic calculus", Springer, 1998.
3) J.-F. Le Gall: "Brownian Motion, Martingales and Stochastic Calculus", Springer, 2016.
4) D. Revuz, M. Yor: "Brownian Motion and Continuous Martingales", Springer, 1999.
Other references will be given during the course.
2) I. Karatzas, S. Shreve: "Brownian motion and stochastic calculus", Springer, 1998.
3) J.-F. Le Gall: "Brownian Motion, Martingales and Stochastic Calculus", Springer, 2016.
4) D. Revuz, M. Yor: "Brownian Motion and Continuous Martingales", Springer, 1999.
Other references will be given during the course.
Assessment methods and Criteria
Oral exam on the course material. During the exam the student will be asked to describe and prove some of the results of the course in order to assess the knowledge and understanding of the course topics as well as the ability to apply them.
MAT/06 - PROBABILITY AND STATISTICS - University credits: 6
Practicals: 12 hours
Lessons: 35 hours
Lessons: 35 hours
Professor:
Campi Luciano
Professor(s)