THE UNIVERSITY OF HONG KONG
FACULTY OF BUSINESS AND ECONOMICS
School of Economics and Finance
ECON6068: Advanced Topics in Applied Econometrics
(Financial Econometrics)
Spring 2017
Instructor: Ka-fu WONG
Office hours: By appointment
Email: [email protected]
Course webpage: Accessible through the HKU portal page (username and passcode required)
COURSE INFORMATION:
Prerequisites: Solid training in Applied Econometrics or equivalent is assumed. Previous training in basic
programming skills are preferred.
Course Overview: Financial econometrics is the intersection of statistical techniques and finance. Financial
econometrics provides a set of tools that are useful for modeling financial data and testing beliefs about how
markets work and prices are formed. Conversely, new techniques in analyzing financial data can lead to empirical
facts inconsistent with existing theories, begging for new models or investment strategies. We focus on several
empirical techniques which are often used in the analysis of financial markets and how they are applied to actual
data.
Course Objectives:
1. To provide an introduction to financial econometrics.
2. To develop the ability to conduct elementary empirical analysis of financial data.
Textbook: We do not rely on any single textbook. The followings are our major references.
• John Campbell, Andrew Lo, Archie MacKinlay (1997): The Econometrics of Financial Markets, Princeton
University Press. (CLM)
• John H. Cochrane (2005): Asset Pricing, Princeton University Press. (JC)
• Hamilton, J. (1994): Time Series Analysis, Princeton University Press, Princeton. (JH)
Computational Software: For empirical work, we often need to write programme code and run the programme code using batch mode. Students are required to use R, a free and popular computing language
(similar to Matlab and S-Plus). R is available at our computer lab at KKL 1009, and may be downloaded from
http://www.r-project.org/. Many researchers have developed R packages for financial analysis. Sample scripts
will be provided in due course to lower the cost of learning the software.
Typesetting Software: In preparing for reports on empirical work, we often include equations, mathematical
symbols and plots. Microsoft Words is a clumsy software to do this job. Fortunately, a free typesetting software
LYX is available, and may be downloaded from http://www.lyx.org/Download. It is highly recommended, but
not required.
INTENDED LEARNING OUTCOMES
On completion of this course, students should be able to:
CILO1.
Collect data, select the appropriate econometric technique to test financial models and estimate
financial relationship, and write a short report based on the results.
CILO2.
Understand and evaluate basic empirical results of research reports.
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ALIGNMENTS OF PROGRAM AND COURSE LEARNING GOALS:
PLO1.
PLO2.
PLO3.
PLO4.
PLO5
Program Intended Learning Goals
Understanding of fundamental theories and new development in economics
Mastering of skills in analyzing economic data
Demonstration of ability to apply economic knowledge and analytical skills
to address policy and business problems
Awareness of ethical concerns in economic issues
Mastering of communication skills
Course ILOs
CILO1, CILO2
CILO1, CILO2
CILO1, CILO2
—
CILO1
TEACHING AND LEARNING ACTIVITIES
TLA1.
TLA2.
TLA3.
TLA4.
TLA5.
Lectures:
Instructor will give lectures on major concepts and issues.
Empirical Projects:
Students will be asked to test financial models and estimate financial relationships using different
datasets and models covered.
Tests:
Tests will be given to assess student’s understanding of the concepts covered and thus ensure
students are on track.
Self-practice exercises:
Self-practice exercises will be given to help students practice what they learn and discover
additional results.
Consultation:
Students are also encouraged to discuss questions with the instructor via email or a forum in the
class website, or by appointment.
ASSESSMENT
A1
Assessment
Empirical Projects
Weight
100%
Empirical Projects: Three projects will be given. The aim of the projects is to let students practice the
empirical analysis of financial data. All projects are meant to be completed individually.
Each student will
::::::::::
have to work on a set of financial time series that is different from the others. By the due date, each student
should upload a zip file containing the report summarizing the findings, the datasets, and programme scripts,
via the class website.
While the detailed assessment rubric may differ slightly across projects, the criteria of assessment can be
broadly divided into two aspects:
(1)
(2)
Statistical Analysis (Logical Reasoning):
Clarity (Readability):
60%
40%
Due to the level of difficulty of the projects, the projects will carry different weights.
Project #1:
Project #2:
Project #3:
20%
35%
45%
Late submissions are accepted within one week of the submission deadline (based on the time stamp appeared
in our course website). A penalty of 10% will automatically apply to late submissions if it is submitted within
one day after the submission deadline, and an additional 10% penalty for each additional day.
One second late is late. Students are responsible in making sure that their projects are uploaded before
the submission deadline. To avoid penalty due to late submissions, students should try to start working on
the assignment early and submit their work slightly earlier than the submission deadline (say, one-day earlier if
they have an unstable network at home).
Self-practice exercises: Self-practice exercises will be given ::::::
weekly. It will not be graded. Answers will be
provided for self-checking. The content of these exercises are often included in the three in-class tests.
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Relationship Among Intended Learning Outcome, Teaching And Learning Activities And Assessments
CILO
1
2
Teaching and Learning Activities
1
2
3
4
!
!
!
!
!
!
!
!
(TLA)
5
!
!
Assessment (A)
1
!
!
STANDARDS OF ASSESSMENT
Overall grades are given using the following criteria approximately:
Grade
A-, A, A+
B-, B, B+
C-, C, C+
D, C+
F
Performance
>90
75-90
60-75
50-60
<50
ACADEMIC CONDUCT
The University Regulations on academic dishonesty will be strictly enforced! Please check the University
Statement on plagiarism on the web: http://www.hku.hk/plagiarism/
Academic dishonesty is behavior in which a deliberately fraudulent misrepresentation is employed in an
attempt to gain undeserved intellectual credit, either for oneself or for another. It includes, but is not necessarily
limited to, the following types of cases:
1. Plagiarism - The representation of someone else’s ideas as if they are one’s own. Where the arguments,
data, designs, etc., of someone else are being used in a paper, report, oral presentation, or similar academic
project, this fact must be made explicitly clear by citing the appropriate references. The references must
fully indicate the extent to which any parts of the project are not one’s own work. Paraphrasing of
someone else’s ideas is still using someone else’s ideas, and must be acknowledged.
2. Unauthorized Collaboration on Out-of-Class Projects - The representation of work as solely one’s own
when in fact it is the result of a joint effort.
Where a candidate for a degree or other award uses the work of another person or persons without due acknowledgment:
1. The relevant Board of Examiners may impose a penalty in relation to the seriousness of the offence;
2. The relevant Board of Examiners may report the candidate to the Senate, where there is prima facie
evidence of an intention to deceive and where sanctions beyond those in (1) might be invoked.
No plagiarism will be tolerated. Please refer to the section of Academic Conduct below for more details.
TENTATIVE CLASS SCHEDULE
1. A brief review of statistics and matrices
2. An introduction to R
3. Principles of Financial Economics
Reading:
• JC: Ch. 1
4. Generalized Method of Moments
Reading:
• JC: Сh. 10-11, 13, 15-16
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• Hansen, L. P. (1982): “Large sample properties of generalized method of moments estimators,”
Econometrica 50(4), 1029–1054.
• Hansen, L. P. and Singleton, K. J. (1982): “Generalized instrumental variables estimation of nonlinear
rational expectations models,” Econometrica 50(5), 1269– 1286.
• Hansen, L. P. and Singleton, K. J. (1988): “ Generalized instrumental variables estimation of nonlinear rational expectations models: Errata,” Econometrica 52(1), 267–268.
• Hansen, L. P. and Jaganathan, R. (1997): “Assessing specification errors in stochastic discount factor
models” Journal of Finance 52, 557-590.
5. Event-study methodology
Abnormal returns, tests on abnormal returns, cross-sectional approach.
Reading:
• CLM: Сh. 4
• Boehmer, E., Musumeci, J. and A. Poulsen (1991): “Event-Study Methodology under Conditions of
Event-Induced Variance,” Journal of Financial Economics 30, 253-272.
• Fama, E., Fisher, L., Jensen, M. and R. Roll (1969): “The Adjustment of Stock Prices to New
Information,” International Economic Review 10, 1-21.
• Prabhala, N. (1997): “Conditional Methods in Event Studies and an Equilibrium Justification for
Standard Event-Study Procedures,” Review of Financial Studies 10, 1-38
• Wan, K.M. and K.F. Wong (2009): “Economic Impact of Political Barriers to Cross-border Acquisitions: An Empirical Study of CNOOC’s Unsuccessful Takeover of Unocal,” Journal of Corporate
Finance 15(4), 447−468.
6. Testing return predictability
Technical trading rules, measures of return predictability, review of test of forecasting power and bootstrap.
Reading:
• CLM: Ch. 2-3
• Bossaerts, P., and P. Hillion (1999): “Implementing Statistical Criteria to Select Return Forecasting
Models: What Do We Learn?” Review of Financial Studies 12, 405-428.
• Fama, E. and K. French (1988): “Dividend Yields and Expected Stock Returns,” Journal of Financial
Economics 22, 3-26.
• Diebold, F. X. and J.A. Lopez (1996): “Forecast evaluation and combination,” in G. Maddala and
C. Rao (eds), The Handbook of Statistics, Vol. 14, Elsevier North Holland.
• Brock, W., Lakonishok, J. and B. LeBaron (1992): “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,” Journal of Finance, 47, 1731–1764.
• Sullivan, R., Timmermann, A. and H. White (1999): “Data-snooping, technical trading rule performance, and the bootstrap,” Journal of Finance 54, 1647–1691.
• White, H. (2000): “A reality check for data snooping,” Econometrica 68, 1097–1126.
7. GARCH
Reading:
• Engle, R. F. (1982): “Autoregressive conditional heteroscedasticity with estimates of the variance of
United Kingdom inflation,” Econometrica 50, 987–1008.
• Bollerslev, T. (1986): “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics 31, 307–327.
• Engle, R. F., Lilien, D. M. and R. P. Robins (1987): “Estimating time varying risk premia in the
term structure: The arch-m model,” Econometrica 55, 391–407.
• Engle, R.F., and A. Patton (2001): “What good is a volatility model?” Quantative Finance 1, 237-245
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