Topics in Probability Theory and Stochastic Processes

BEA513 Topics in Probability Theory and Stochastic Processes

Autumn 2021

  • Topics

    This course is an introductory course in stochastic analysis and focuses on developing students’ knowledge and understanding of dynamic systems. Static models generally fail to explain changes in the economy, and the time development of dynamical systems is crucial to understand how and why systems change. Geometric Brownian motion and the Ornstein-Uhlenbeck process are widely used in applications of this theory, and the students should be familiar with the construction of these processes. The part of stochastic analysis covered in this course, is a prerequisite for many different topics in economics and management science, and is a must for studies in mathematical finance

    Topics covered:

    1. Basic properties of Brownian motion
    2. Numerical simulation of Brownian motion
    3. Calculating expected values related to Brownian increments
    4. Filtrations and filtered information
    5. Stochastic integrals
    6. Numerical simulations of stochastic integrals
    7. The Ito formula
    8. Geometric Brownian motion
    9. The Ornstein-Uhlenbeck process
    10. Numerical schemes for stochastic differential equations
    11. Calculating conditional expectations
    12. Applications to continuous time newsvendor models

  • Learning outcome

    After completion of the course, the students should:


    • understand dynamic systems and be able to describe how and why systems change.


    • be able to compute stochastic integrals, analytically by use of the Ito formula, and numerically via the Euler and Milstein schemes.
    • be able to solve stochastic differential equations analytically as well as numerically and to compute conditional expectations based on filtered information.
    • Be able to use geometric Brownian motions and the Ornstein-Uhlenbeck processes.

    General competence

    • have general competence in stochastic analysis and consider applications to problems in economics.

  • Teaching

    Regular lectures/exercises solved within group. Due to the present corona situation, the course will be taught digitally via lectures in Zoom if campus is closed.

  • Restricted access

    PhD students at NHH have access to this course. Other students can be granted access by application if there is sufficient capacity.

  • Recommended prerequisites

    As much mathematics as possible, particularly relevant are the courses MAT10 and 11.

  • Requirements for course approval

    Two compulsory assignments

  • Assessment

    Oral presentation of individual topic

  • Grading Scale


  • Computer tools

    Any suitable programming language

  • Literature

    Bernt Øksendal: Stochastic Differential Equations, Springer.


ECTS Credits
Teaching language

Autumn. Offered when there is sufficient demand (4 students).

Course responsible

Jan Ubøe, Dept of Business and Management Science