Dynamic Asset Allocation

Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management

Teaching language: English
EKA: B560032102
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master

Course ID: B560032101
ECTS value: 10

Date of Approval: 25-09-2018


Duration: 1 semester

Course ID

B560032101

Course Title

Dynamic Asset Allocation

Teaching language

English

ECTS value

10

Responsible study board

Study Board of Market and Management Anthropology, Economics, Mathematics-Economics, Environmental and Resource Management

Date of Approval

25-09-2018

Course Responsible

Name Email Department
Christian Riis Flor crf@sam.sdu.dk

Offered in

Odense

Level

Master

Offered in

Spring

Duration

1 semester

Mandatory prerequisites

None.

Recommended prerequisites

This course requires that the student has prior knowledge of financial markets, financial instruments, and derivatives. In particular knowledge about introductory asset allocation; that is, a thorough understanding of the mean-variance analysis. These are all competences acquired in the course Finansiering, investering og virksomhedsstrategi (9161001) which is based on the textbooks:

  • David Hillier, Mark Grinblatt and Sheridan Titman: Financial Markets and Corporate Strategy, European Edition, Irwin/McGraw-Hill, 2012.
  • Christian R. Flor and Linda S. Larsen: Indledende porteføljevalgsteori, teaching note, latest edition.

Furthermore, the student should be able to explain and apply Itô calculus. A basic understanding of term structure models is also highly recommended. For example, knowledge about the Vasicek and CIR term structure models is expected. The student should also be familiar with numerical methods, like Monte Carlo simulation. These are all competences acquired in the course Derivatives and Risk Management (9427401) which are based on the textbooks:

  • John Hull: Options, futures and other derivatives, Pearson, (newest edition).
  • Claus Munk: Fixed income modelling, Oxford, 1st edition, (newest edition).

    The student should have an elementary background in mathematics and probability theory. In particular, the student should be able to compute expectations, variances, and covariances of random variables whether their distribution is discrete (e.g. binomial) or continuous (e.g. normal).

    Finally, it is highly recommended that the student is familiar with vector and matrix notation and know how to solve a system of linear equations. These are all competences acquired in the courses Matematik (9105701) and Statistik (9116001) which are based on the textbooks:

    • Knut Sydsaeter and Peter Hammond, Essential Mathematics for Economic Analysis, Pearson Education, 3rd edition 2008.
    • Malcow-Møller, N. og Allan Würtz "Indblik i Statistik", latest edition.

    Aim and purpose

    To give the students a thorough understanding of modern models of investment problems in financial assets and their solutions. After a critical review of the classic mean-variance framework, multi-period models for portfolio management are introduced. General results on diversification, fund separation, and intertemporal hedging are derived and discussed. A number of specialized models for the long-term investment problems of individuals and households are analyzed in detail; for example, models featuring interest rate and inflation uncertainty, labor income risk, and life-cycle aspects. Finally, it will be discussed whether investment professionals and private investors follow the guidelines of the normative theories developed and discussed in class. As an illustration, the class also looks at household portfolios and discusses the appropriateness of investment advice and thereby briefly touches on some issues of behavioral finance.

    The framework used to analyze dynamic asset allocation problems is related to the one used to analyze dynamic corporate investment and capital structure problems; thus, the course complements other graduate courses such as Dynamic Corporate Finance and Investments

    Content

    1. Decision-making in multi-period models with uncertainty
    2. Dynamic programming and stochastic control with a focus on asset allocation (portfolio choice) problem
    3. Contrast normative theories with positive (empirical) investor behavior
    4. Optimal consumption and investment decisions of individuals, e.g.
      • review of mean-variance analysis
      • dynamic model with constant investment opportunities
      • dynamic model with stochastic interest rates
      • dynamic model with stochastic market price of risk
      • effects of labor income, housing, etc.
      • non-standard utility or information structures may be considere

    Learning goals

    To fulfill the purposes of the course the student must be able to:

    Description of outcome - Knowledge

    Demonstrate knowledge about the course’s focus areas enabling the student to:

    • Explain the idea of dynamic programming and the derivation of the Bellman equation in discrete-time models and the Hamilton-Jacobi-Bellman equation in continuous-time models of typical financial optimization problems.
    • Describe and explain the one-period mean-variance analysis framework for optimal portfolio choice.
    • Describe and explain a general and various concrete diffusion-type models for utility-maximizing dynamic consumption and portfolio strategies.
    • Solve the associated Hamilton-Jacobi-Bellman equations for the different models considered, interpret and explain the conclusions; discuss and criticize the assumptions behind the models.
    • Compare the solutions to diffusion-type models for utility-maximizing dynamic consumption and portfolio strategies to typical investment advice

    Description of outcome - Skills

    Demonstrate skills, such that the student is able to:

    • Apply the standard Hamilton-Jacobi-Bellmann equation in continuous-time models of typical financial optimization problems
    • Analyze and criticize the one-period mean-variance analysis framework for optimal portfolio choice
    • Analyze a general diffusion-type model for utility-maximizing dynamic consumption and portfolio strategies, e.g. set up the associated Hamilton-Jacobi-Bellman equation.
    • Analyze and criticize various concrete diffusion-type models for utility-maximizing dynamic consumption and portfolio strategies, solve the associated Hamilton-Jacobi-Bellman equations, interpret and explain the conclusions.
    • Be able to implement optimal investment strategies numerically
    • Discuss and criticize the assumptions made for the applications and interpretation of the results.
    • Reflect upon the conclusions obtained by the analysis in the different applications.

    Description of outcome - Competences

    Demonstrate competences, such that the student is able to:

    • Independently apply models and theories related to dynamic asset allocation for individual investors.
    • Identify a need for further development of the models and theories related to dynamic asset allocation.
    • Independently apply dynamic asset allocation theories and models and use this to develop models, in new, but related, topics 
    • Use the above knowledge and skills to participate in team work so that the student obtains competences in collaboration and communication.

    Literature

    Example:

    Munk, C.: "Dynamic asset allocation", Lecture notes, Copenhagen Business School, newest edition, or similar material.

    Articles and additional lecture notes.

    Teaching Method

    A total of 46 hours of lectures during a 15 week period. 
    Project work in approximately 3-4 intervening weeks of the semester. 

    Workload

    Scheduled classes:
    2x2 hours weekly. 
    A total of 46 hours of lectures during a 15 week period. 
    Project work in approximately 3-4 intervening weeks of the semester. 

    Workload:
    The teaching activities result in an estimated distribution of the work effort of an average student as follows:
    Lectures: 46 hours
    Preparation, lectures: 143 hours
    Assignments: 80 hours
    Oral examination: 1 hour
    Total 270 hours

    Examination regulations

    Exam

    Name

    Exam

    Timing

    Two assignments:

    Exam: During the semester
    Reexam: August


    Oral examination:

    Exam: June
    Reexam: August

    Tests

    Home assignments with oral examination

    Name

    Home assignments with oral examination

    Form of examination

    Home assignment with oral defense

    Censorship

    Second examiner: Internal

    Grading

    7-point grading scale

    Identification

    Student Identification Card - Date of birth

    Language

    English

    Duration

    Two assignments:

    Approximately 2 weeks pr. assignment. Date for submission will appear from the examination plan.

    Oral examination:

    20 minutes with 20 minutes of preparation.

    Length

    Maximum 10 pages pr. assignment. 

    A standard page is 2400 keystrokes including spacing. List of contents, references and appendices are not included in the number of pages. One figure or table equals 400 keystrokes. It must be stated how many keystrokes, figures and tables the report consists of. 

    Examination aids

    Assignments:

    All exam aids allowed.

    Oral examination:

    Only hand-written notes which are written during the preparation are allowed during the oral examination.

    Assignment handover

    The course page in Blackboard.

    Assignment handin

    Via SDUassignment in the course page in Blackboard.

    ECTS value

    10

    Additional information

    The assignments have to be solved in groups of 2-3 students, unless the instructor explicitly grants an exemption from this rule. The instructor has allocation rights to form groups.

    The oral examination is based on the two assignments, a randomly drawn topic, and potentially questions in other parts of the syllabus.

    Examination form at the re-exam can be changed.

    EKA

    B560032102

    External comment

    NOTE - This course is identical with the former course 9851801 Dynamic Asset Allocation.
    Used examination attempts in the former identical course will be transferred.
    Courses that are identical with former courses that are passed according to applied rules cannot be retaken.

    The student is automatically registered for the first examination attempt when the student is registered for a course or course element with which one or more examinations are associated. Withdrawal of registration is not possible, and students who fail to participate in an examination have used one examination attempt, unless the University has made an exemption due to special circumstances. 


    Courses offered

    Period Offer type Profile Programme Semester
    Spring 2019 Mandatory Master of Science in Economics (with profile in Finance) MSc in Economics | Master of Science (MSc) in Economics | Odense 2
    Spring 2019 Optional Master of Science in Economics (with profile in Health Care Management and Economics) MSc in Economics | Master of Science (MSc) in Economics | Odense
    Spring 2019 Optional Master of Science in Economics (with profile in Finance and Economics) MSc in Economics | Master of Science (MSc) in Economics | Odense
    Spring 2019 Optional Master of Science in Economics (with profile in Microeconomics) MSc in Economics | Master of Science (MSc) in Economics | Odense
    Spring 2019 Optional Master of Science in Economics (with profile in Accounting and Economics) MSc in Economics | Master of Science (MSc) in Economics | Odense
    Spring 2019 Optional Master of Science in Economics (with profile in Macroeconomics: Growth and Fluctuations) MSc in Economics | Master of Science (MSc) in Economics | Odense
    Spring 2019 Optional Master of Science in Economics (with profile in Economics) MSc in Economics | Master of Science (MSc) in Economics | Odense
    Spring 2019 Optional Master of Science in Economics (with profile in Economics and Project Management) MSc in Economics | Master of Science (MSc) in Economics | Odense
    Spring 2019 Exchange students

    Teachers

    Name Email Department City
    Steffen Meyer stme@sam.sdu.dk Odense

    URL for MySchedule