GRA 6560 Strategic Asset Allocation

GRA 6560 Strategic Asset Allocation

Course code: 
GRA 6560
Department: 
Finance
Credits: 
6
Course coordinator: 
Costas Xiouros
Course name in Norwegian: 
Strategic Asset Allocation
Product category: 
Master
Portfolio: 
MSc in Quantitative Finance
Semester: 
2021 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

Strategic asset allocation (SAA) is one of the most challenging problems in finance. It deals with long-term investment, that is, how households, pension funds, and sovereign wealth funds--for example the Norwegian sovereign wealth fund that is managed by Norges Bank Investment Management--should allocate their wealth across large asset classes. The difficulty of the problem stems partly from the lack of sufficient data to be able to assess different strategies over long periods of time. For this reason, state-of-the-art practices require a deep understanding of both static and dynamic asset pricing theories, good knowledge of empirical facts, and the ability to combine the above with econometric and advanced financial modeling. 

In this course, we will go over key elements, theories, and modern practices of SAA. We will start with the objective, framework, and performance measurement issues of SAA. We will cover practical aspects of mean-variance investing, we will then move to dynamic theories and models, then to the modern approach of factor investing, "alpha" generation, and real assets. The course oscillates between the normative approach of maximizing the utility of an investor and the positive approach of trying to understand the statistical properties of asset prices and returns. 

Learning outcomes - Knowledge

The students by the end of the course will know

  • The main elements and importance of an asset allocation strategy
  • The main theories, both static and dynamic, regarding the equilibrium relation between risk and return, underpinning strategic asset allocation
  • Relevant empirical and historical facts about asset returns that are necessary to consider for strategic asset allocation
Learning outcomes - Skills

The students by the end of the course will be able to

  • Formulate an asset allocation problem, characterize the optimal strategy, and understand the connection between optimal strategy and assumptions
  • Implement, evaluate the past performance, and simulate the future performance of a strategy
General Competence

The students by the end of the course will be able to analyze an asset allocation problem, tailored to the needs of the investor, and propose and present clearly and coherently a strategy.

Course content

The course will cover the following topics (provided enough time):

  • The problem and objective of strategic asset allocation
  • Static investment strategies
  • Dynamic asset allocation
  • Factor theory, factors, and factor investing
  • Real assets
  • Generating "alpha"
Teaching and learning activities

The class will be interactive and highly practical. During classes, we will be going over relevant theories, models, cases, different approaches and strategies of asset allocation, as well as empirical facts.  There will be discussions and student presentations. Some parts of the course are highly technical and we will also program in Python.

No knowledge of Python is required prior to the course, but the programming knowledge gained in R will be important.

There will be a lot of group work. The students will be expected to participate actively in class, solve cases, present topics, and perform a group project on strategic asset allocation. The group should work on the project over the entire semester under the supervision of the teacher. At the end of the course, the groups will present their results.

The course is designed, not only to develop certain technical skills and learn relevant methods but most importantly to combine these skills with the insight required to evaluate asset allocation strategies and be able to present them in a clear and coherent manner.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

This is a course with continuous assessment (several components) and one final exam code. Each component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course start.

At resit, all exam components must, as a main rule, be retaken during next scheduled course.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Covid-19 

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Teaching 

Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

This course assumes that students have followed the asset management track, that is, that they have taken Investments, Asset Management, and Fixed Income Securities, or similar courses. Also, the students are expected to know well the mathematics, statistics and programming taught during the first year.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
15
Grouping: 
Group (3 - 4)
Duration: 
1 Semester(s)
Comment: 
Group assignment: One case but this may vary.
Exam code: 
GRA65601
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Activity
Form of assessment: 
Presentation
Weight: 
10
Grouping: 
Group (3 - 4)
Comment: 
Group activities: Presentation on a class-specific topic
Exam code: 
GRA65601
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
30
Grouping: 
Group (3 - 4)
Duration: 
1 Semester(s)
Comment: 
Group project on strategic asset allocation: The report and supporting material
Exam code: 
GRA65601
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Activity
Form of assessment: 
Presentation
Weight: 
10
Grouping: 
Group (3 - 4)
Comment: 
Group project on strategic asset allocation: Presentation of the report results
Exam code: 
GRA 65601
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
10
Grouping: 
Individual
Duration: 
1 Semester(s)
Comment: 
Individual assessment regarding group project and presentation.
Exam code: 
GRA65601
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Invigilation
Weight: 
20
Grouping: 
Individual
Support materials: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration: 
1 Hour(s)
Comment: 
Mid-term school exam under supervision
Exam code: 
GRA65601
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
5
Grouping: 
Individual
Duration: 
1 Semester(s)
Comment: 
Individual assessment regarding topic presentation and group assignment.
Exam code: 
GRA&5601
Grading scale: 
Point scale leading to ECTS letter grade
Resit: 
All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
24 Hour(s)
Digital resources
12 Hour(s)
Asynchronous teaching activities like videos, walk-through programming scripts, online discussions, and quizzes with feedback.
Student's own work with learning resources
54 Hour(s)
Weekly studying and mid-term preparation.
Group work / Assignments
80 Hour(s)
Work on group assignments, projects, and presentations.
Sum workload: 
170

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.