25861 Empirical Asset Pricing
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Description
The goal of this subject is to establish perspectives, approaches, tools and methods of independent thinking, analysis, and problem solving and their application to essential asset pricing topics. Topics include utility theory, portfolio theory, arbitrage pricing, equilibrium pricing, security prices' informational efficiency, and performance measurement.
For more information, contact your PhD supervisor.
Subject learning objectives (SLOs)
| 1. | Understand fundamental probability concepts used in econometric analysis; |
|---|---|
| 2. | Describe key classical econometric assumptions and the effects of the violations of those assumptions; |
| 3. | Describe the principles of least squares analysis and the properties of least squares estimators; |
| 4. | Understand properties of variables observed in financial markets, such as stock prices and interest rates. |
| 5. | Combine finance theory and econometrics tools to design research on financial markets, in particular, developing, estimating and analysing least square regressions to study relations among financial variables; |
| 6. | Collect, interpret, and organize financial data; |
| 7. | Write programming codes for statistics/mathematics software, such as Matlab; |
| 8. | Interpret and analyse key statistics and diagnostics generated by the software; |
| 9. | Explain verbally and in writing implications of empirical results for finance theory; and |
| 10. | Collaborate with other students to study issues in financial markets using econometrics tools. |
Teaching and learning strategies
The course focuses on learning fundamental principles of least squares analysis, the most commonly used methodology in econometrics. Understanding the concepts and developing econometrics intuition are very important to apply the knowledge to empirical research on financial markets. The course also covers the probability theory to help students build a rigorous foundation for learning econometrics. The course will deal with many examples of how least square analysis is used to study interesting issues in financial markets.
Lectures and lecture slides cover the main material. Students are required to attend the lectures. Lecture slides are based on the textbook and the recommended readings. Lecture slides will be posted on Blackboard. Students are encouraged to print out lecture slides and take notes on them in class. At the end of each lecture, students will solve quizzes (not graded) and discuss the answers.
Bi-weekly homework assignments are designed to help students understand the concepts and develop econometrics intuition. Reviews of lecture slides and the textbook are critical for solving the questions. Students are encouraged to discuss with one another for the assignments. The assignments will also be helpful to prepare for the final exam.
Students will conduct empirical research on financial markets in groups. Group members are expected to discuss potential topics for their projects. All members are also expected to contribute to the final report equally through the following activities: design empirical research, gather data, conduct least squares analyses, interpret the results, and discuss implications of the results. Therefore, rigorous understanding of the course materials and good teamwork are critical for successful projects.
There will be at least two lab sessions for learning Matlab. Matlab is a mathematics software, which is widely used in finance and economics and requires good knowledge of econometrics. Students are required to use the software to solve some of the assignment questions and conduct empirical analysis for the group project. Students need to study all materials covered in class for the final exam.
Assessment
Assessment task 1: Assigments (Individual)
| Weight: | 25% |
|---|---|
| Criteria: | Homework assignments will help students understand materials covered in the most recent two lectures. Each assignment has a weight equal to 5% of the final grade. Scores for each question are from 0 (not answered) to 5 (perfect). Good answers but not perfect earn 4. Students are encouraged to work together to discuss the questions but required to submit their own answers. Both handwriting and typing are accepted. Answers must be clearly written. |
Assessment task 2: Project (Group)
| Weight: | 35% |
|---|---|
| Criteria: | A group project combines econometrics techniques covered in the course and finance knowledge. Examples discussed in class will be good samples of group projects. Students need to form groups of at least 2 members but no more than 4 members. Grading depends on a number of things, such as application of econometrics techniques to issues in financial markets, programming skills for Matlab, interpretation of the software outputs, and presentation of the results including tables, figures and main texts. |
Assessment task 3: Final Exam
| Weight: | 40% |
|---|---|
| Criteria: | The final exam will cover all materials learned in class except for Matlab lab sessions. Most of questions will ask to explain concepts and principles and interpret analysis results. |
Required texts
- A Guide to Econometrics, Peter Kennedy, 6th Edition, 2008, Wiley-Blackwell
- Introduction to Econometrics, Stock and Watson, 2nd Edition
Recommended texts
- The Econometrics of Financial Markets, Campbell, Lo, and MacKinlay, 1996
- Introductory Econometrics: a modern approach, Wooldridge, 3rd edition
