MVE565 / MMA630 Computational methods for stochastic differential equations

This page contains the program of the course: lectures, exercise sessions and projects. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.


The schedule of the course is in TimeEdit.


Below you find the preliminary schedule based on the last iteration of the course. We will adapt the speed to the group and interest of the participants.

Day Sections Content Questions

Slides & notes

17/1 ([G] Chp. 1-3) Introduction

annotated slides

20/1 [G] 4.1 - 4.2 Brownian motion, Itô integral Questions Lecture 2
24/1 [G] 4.3 stochastic differential equations Questions Lecture 3
27/1 [G] 4.4 - 4.5, 5.1

Feynman-Kac formulas
Euler-Maruyama scheme

Questions Lecture 4

[G] 5.1 - 5.2

2 (new to you)

Euler-Maruyama scheme, strong convergence

Monte-Carlo methods

Questions Lecture 5

[G] 5.3(-5.4), 6.1

Weak convergence

statistical errors

Questions Lecture 6
7/2 [G] 6.2-6.3

 (Multilevel) Monte Carlo

Questions Lecture 7
10/2 Project 1: questions (Per)

[G] 4-6

Repetition [G]

Questions Lecture 8
17/2 [HRSW] 3.1 - 3.3

Sobolev spaces, variational formulation, Gelfand triplet, discretization

Questions Lecture 9
21/2 [HRSW] 3.4-3.6 Implementation, stability and error analysis Questions Lecture 10 Comment on proof for CN, Thm. 3.6.5
22/2   Discussion Project 1
24/2 [HRSW] 4.1-4.4
BS SDE and PDE, variational formulation, localization, discretization Questions Lecture 11
28/2 [HRSW] 4.5, 8, 9 Extensions of BS to other models and higher dimensions (brief) Questions Lecture 12
3/3 [HRSW] Repetition [HRSW] and all remaining questions for both parts of the course Questions Lecture 13


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Recommended exercises

Preliminary list of exercises that might be adapted.

Day Exercises
18/1 self preparation for lecture






10/2 Questions for Project 1


22/2 Discussion Project 1 (Annika)


Exercises and hints for exercise sessions: Here


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Two projects can be handed in for up to four bonus points per project on the ordinary exam. Reports are written and submitted individually.
Project 1: deadline February 14, 2022, 07:00
Project 2: deadline March 10, 2022, 07:00

Reference literature:

  1. [G] Emmanuel Gobet: Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear, CRC, 2016

  2. [HRSW] Norbert Hilber, Oleg Reichmann, Christoph Schwab, Christoph Winter: Computational Methods for Quantitative Finance: Finite Element Methods for Derivative Pricing, Springer, 2013
  3. [KP] Peter Kloeden, Eckhard Platen: Numerical Solutions of Stochastic Differential Equations, Springer, 1992
  4. [Ø] Bernt Øksendal: Stochastic Differential Equations: An Introduction with Applications, Springer, 2003
  5. Learning MATLAB, Tobin A. Driscoll. Provides a brief introduction to Matlab to the one who already knows computer programming. Available as e-book from Chalmers library.
  6. Physical Modeling in MATLAB 3/E, Allen B. Downey
    The book is free to download from the web. The book gives an introduction for those who have not programmed before. It covers basic MATLAB programming with a focus on modeling and simulation of physical systems.

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Course summary:

Date Details Due