# GRA 6560 Strategic Asset Allocation

## GRA 6560 Strategic Asset Allocation

This is an advanced course that will cover the main theories developed in the finance literature to understand asset prices. The models include both static (CAPM, APT) and dynamic (ICAPM, CCAPM and extensions, Production-based) asset pricing theories. Empirical evidence of the application and performance of the theories will also be examined. The construction of these theories is underlined by portfolio strategies that aim to optimize the relation between risk and return, given investor preferences and the investment opportunity set. The approach of 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. This course will prepare the students to use advanced asset pricing theories and dynamic portfolio allocation tools to develop asset allocation strategies for personal, sovereign and institutional wealth, and asset management. The toolset includes statistical analysis of asset pricing data, development and quantitative analysis of theoretical models, and programming tools for solving and simulating optimal allocation strategies.

The student by the end of the course will know

- Classical asset pricing theories developed to understand the relation between risk and return for securities over time.

The student at the end of the course will be able to

- Apply and statistically evaluate the performance of some asset pricing theories using real world data digitally collected by students.
- Formulate and solve a portfolio optimization problem based on given asset classes, objectives of investors/funds, and historical data.

The student at the end of the course will be able to

- Analyze asset allocation problems (in both a local and global setting) and provide implementable and practical solutions for sustainable long term performance
- Critically assess various asset pricing theories through appropriate data analysis

The course will cover the following topics:

- Static asset allocation
- CAPM, APT and Consumption CAPM
- Intertemporal CAPM
- Production based Asset Pricing
- Empirical evidence
- Asset pricing puzzles
- Dynamic asset allocation
- Extensions of Consumption CAPM
- Predictability and the Sum-of-Parts methodology

Lectures, class discussions, and assignments. Students are expected to prepare for the lecture by reading assigned materials and participate actively in the discussions. There will be assignments throughout the course. Most learning will take place through student discussions of papers, theories, and other assignments. While a compulsory textbook is listed, the lectures will not strictly follow chapters of the book. The instructor will provide lecture notes, material, examples, and exercises that will help the student understand the topics discussed in class. Please note that it is the student’s own responsibility to obtain any information provided in class that is not included on course homepage/itslearning.

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.

Excel, SAS, STATA

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 specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Students are assumed to have a good understanding of undergraduate-level math, algebra, differential calculus, and mathematical optimization. Understanding of the fundamentals of economics and finance covered in the 1st year of the master’s program are also assumed. If not yet familiar with statistical programing languages (e.g., SAS, STATA, Matlab, etc.,), the student must have self-learning skills.

Exam category | Weight | Invigilation | Duration | Support materials | Grouping | Comment exam |
---|---|---|---|---|---|---|

Exam category:Submission Form of assessment:Written submission Exam code:GRA65601 Grading scale:Point scale Grading rules:Internal examiner Resit:All components must, as a main rule, be retaken during next scheduled course | 45 | No | 1 Semester(s) | Group/Individual ( 1 - 5) | The assessment will consist of a combination of: • A (group) project backtesting and analyzing an asset management strategy • A (group) project that replicates and tests main methodologies of relevant finance papers Group size may vary depending on class size. Instructor will inform students about specific group policies once the semester begins. | |

Exam category:Submission Form of assessment:Written submission Exam code:GRA65601 Grading scale:Point scale Grading rules:Internal and external examiner Resit:All components must, as a main rule, be retaken during next scheduled course | 55 | Yes | 3 Hour(s) | - BI-approved exam calculator
- Simple calculator
- Bilingual dictionary
| Individual | Final written examination under supervision |

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.