Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Finance 2: Dynamic Portfolio Choice (Fin2)

Practical information
Study year 2016/2017
Block 4
Programme level Full Degree Master
Course responsible
  • Rolf Poulsen (4-83807d77517e7285793f7c863f757c)
Phone: +45 35 32 06 85 office, 04.4.11
  • Department of Mathematical Sciences
Course number: NMAA09045U

Course content

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

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.

Recommended prerequisites

A bachelor degree in Mathematics-Economics.

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MSc Programme in Mathematics-Economics


Study Board of Mathematics and Computer Science

Course type

Single subject courses (day)


1 block


---- SKEMA LINK ----

Teaching and learning methods

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


No limit




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


Type of assessment

Oral examination, 20 min
without preparation time


All aids allowed

Marking scale

7-point grading scale

Criteria for exam assessment

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

Censorship form

External censorship


Same as ordinary

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