NMAA09045U Finance 2: Dynamic Portfolio Choice (Fin2)
Volume 2023/2024
Education
MSc Programme in Mathematics-Economics
Content
See the "Knowledge" part of the learning outcome below.
Learning Outcome
Competencies
- Formulate and analyze decision problems (investment/consumption and optimal stopping) in a stochastic multi-period setting.
- Analyze model consequences “with numbers”; algorithmically, experimentally or empirically. (As well as understand why these three things are different concepts.)
- 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.
Skills
- 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.
Knowledge
- A closer look at arbitrages: No arbitrage-intervals in incomplete markets, cross-currency betting arbitrage, statistical arbitrage.
- Maximization of expected utility and (partial) equilibrium in one-period models, the state-price utility theorem and betting against beta.
- Multi-period optimal portfolio choice. The martingale method vs. dynamic programming/the Bellman equation.
- Explicit solutions in binomial(‘ish) models and in amodel with reurn preditability and transaction costs.
- Properties and consequences of solutions; myopia and constant weights, C-CAPM, the equity premium puzzle.
- The numeraire porttfolio.
- Optimal stopping and the hedging and pricing of American options including Longstaff and Schwartz' simulation technique.
Recommended Academic Qualifications
A bachelor degree in
Mathematics-Economics.
Academic qualifications equivalent to a BSc degree is recommended.
Academic qualifications equivalent to a BSc degree is recommended.
Teaching and learning methods
4 hours of lectures and 2
hours of tutorials per week for 7 weeks.
Workload
- Category
- Hours
- Lectures
- 28
- Preparation
- 163
- Theory exercises
- 14
- Exam
- 1
- Total
- 206
Feedback form
Oral
Collective
Feedback by final exam (In addition to the
grade)
Sign up
Self Service at KUnet
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 20 minutes
- Type of assessment details
- Without preparation time, but "open book" (i.e. "all aids allowed").
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
Same as ordinary
Criteria for exam assesment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.
Course information
- Language
- English
- Course code
- NMAA09045U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 4
- Schedule
- C
- Course capacity
- No limit
The number of seats may be reduced in the late registration period
Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Rolf Poulsen (4-75726f69437064776b316e7831676e)
Office, 04.4.11
Saved on the
28-02-2023