- Stochastic processes, martingales, local martingales, stopping times, filtrations, Markov properties.
- Brownian motion.
- The Ito isometry, Ito integrals, Ito’s formula.
- Existence and uniqueness of solutions to stochastic differential equations.
- Diffusion processes.
- Girsanov’s theorem.
- Probabilistic representations of solutions to partial differential equations.
- The FeynmanKac formula, Kolmogorov’s forward and backward equations, recurrence, invariant densities.
- More advanced topics, e.g., local times, if time permits.
FSF3713 Stochastic Analysis 7.5 credits
Information per course offering
Information for Spring 2024 Start 16 Jan 2024 programme students
- Course location
KTH Campus
- Duration
- 16 Jan 2024 - 3 Jun 2024
- Periods
- P3 (3.0 hp), P4 (4.5 hp)
- Pace of study
25%
- Application code
60782
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
PhD students only.
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
Contact
Fredrik Viklund (frejo@kth.se)
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FSF3713 (Spring 2019–)Content and learning outcomes
Course contents
Intended learning outcomes
- Understand and explain the following concepts: filtration, stochastic process in continuous time, local martingale, martingale, stopping time, quadratic variation.
- Sketch at least one construction of Brownian motion.
- Construct the Ito integral in some generality, and discuss its basic properties.
- Explain Ito’s formula and use it for practical computations of, e.g., Ito integrals.
- Discuss basic properties of stochastic differential equations (SDEs), in particular diffusions in one dimension.
- Explain Girsanov’s theorem.
- Discuss connections between the theory of SDEs and partial differential equations.
- Solve problems and discuss current research connected to the theory presented in the course
Literature and preparations
Specific prerequisites
Basic probability theory (e.g., SF3940) and basic knowledge in analysis and linear algebra, especially measure theory and Lebesgue integration.
Recommended prerequisites
Equipment
Literature
For example
KaratzasShreve “Brownian motion and stochastic calculus” (ISBN 978-1-4612-0949-2),
RevuzYor “Continuous martingales and Brownian motion” (ISBN 978-3-662-06400-9);
Öksendal “Stochastic Differential Equations” (ISBN 978-3-64214394-6)
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- INL1 - Assignment, 3.5 credits, grading scale: P, F
- TENM - Oral exam, 4.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Homework assignments and oral/written exam.
Other requirements for final grade
Homework assignments completed and oral/written exam passed.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Offered by
Main field of study
Education cycle
Add-on studies
Contact
Supplementary information
PhD students only.