# Syllabus for Measure Theory and Stochastic Integration

Måtteori och stokastisk integration

A revised version of the syllabus is available.

## Syllabus

• 5 credits
• Course code: 1MA051
• Education cycle: Second cycle
• Main field(s) of study and in-depth level: Mathematics A1F, Financial Mathematics A1F
• Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
• Established: 2007-03-15
• Established by: The Faculty Board of Science and Technology
• Revised: 2013-04-23
• Revised by: The Faculty Board of Science and Technology
• Applies from: Autumn 2013
• Entry requirements:

120 credits including Integration Theory, 10 credits, or Measure and Integration Theory I, 5 credits.

• Responsible department: Department of Mathematics

## Learning outcomes

In order to pass the course (grade 3) the student should be able to

• interpret Brownian motion as a stochastic process on a filtered measurable space;
• describe the class of continuous martingales;
• describe the construction of a stochastic integral;
• use Ito's formula;
• describe the concept of "quadratic variation" and the martingale characterisation of Brownian motion;
• formulate the representation theorem for martingales and how to use it;
• formulate the existence and uniqueness theorems for stochastic differential equations;
• use diffusion processes as a tool for mathematical modelling;
• explain the connection between diffusion processes and solutions of parabolic and elliptic partial differential equations;
• use Girsanov's representation theorem.

## Content

Brownian motion. Stochastic integration. Ito's formula. Continuous martingales. The representation theorem for martingales. Stochastic differential equations. Diffusion processes. Girsanov's representation theorem. Applications from selected areas.

## Instruction

Lectures and problem solving sessions.

## Assessment

Compulsory assignments during the course.

Applies from: Autumn 2013

Some titles may be available electronically through the University library.

• Øksendal, Bernt Stochastic differential equations : an introduction with applications

6. ed.: Berlin: Springer, 2003

Find in the library

Mandatory