Finance 2: Dynamic Portfolio Choice (Fin2)

Course content

See the "Knowledge" part of the learning outcome below.


MSc Programme in Mathematics-Economics

Learning outcome


  1. Formulate and analyze decision problems (investment/consumption and optimal stopping) in a stochastic multi-period setting.
  2. Analyze model consequences “with numbers”; algorithmically, experimentally or empirically. (As well as understand why these three things are different concepts.)
  3. Acquire the confidence to read presentations of the same – or almost the same – problem in the literature. Know that notation, motivation, and rigour varies and that there is rarely a gospel.   


  • Rigorously prove optimality principles and conditions for stochastic control problems in (discrete time, finite space)-multi-period setting.
  • Explicitly solve simple investment/consumption and optimal stopping problems.   
  • Derive (with pen and paper), analyze (with a computer) and explain (in plain English) model implications; be they quantitative or qualitative, be they regarding policy, equilibrium, or empirics.


  • Maximization of expected utility and (partial) equilibrium in one-period models, including betting against beta.
  • Multi-period optimal portfolio choice. The martingale method vs. dynamic programming/the Bellman equation.
  • Explicit solutions with HARA utility and binomial(‘ish) stock dynamics. 
  • Properties and consequences of solutions; myopia and constant weights, C-CAPM, the equity premium puzzle.
  • Optimal stopping and the hedging and pricing of American options.

4 hours of lectures and 2 hours of tutorials per week for 7 weeks.

A bachelor degree in Mathematics-Economics.

7,5 ECTS
Type of assessment
Oral examination, 20 minutes
Without preparation time, but "open book" (i.e. "all aids allowed").
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Theory exercises
  • 14
  • Exam
  • 1
  • Preparation
  • 163
  • English
  • 206