Postgraduate Course: Financial Econometrics (ECNM11100)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Financial Econometrics is designed to help participants develop an in-depth understanding of
the characteristics of financial econometric models, their connection to underlying financial
theory, and the role of econometric tools of inference. |
| Course description |
Participants will be guided through key areas including the interdependencies of markets, the
dynamics within financial data, the behaviour of asset prices, volatility in financial returns, and
an introduction to machine learning in financial markets.
Participants will gain practical knowledge and skills in utilising financial econometric models
and techniques to analyse and interpret financial data, as well as their applications in realworld
financial decision-making.
Participants will receive in-class exercises to practise their newly acquired knowledge. They will
be provided with Stata code and data files that tie in closely with the lecture examples. Through
these exercises, participants will be able to acquire hands-on experience working with various
types of data such as stock prices, exchange rates, trading revenues, dividends, earnings, and
tick-by-tick data.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students should be enrolled on MSc Economics, MSc Economics (Econometrics), MSc Economics (Finance) or MSc Mathematical Economics and Econometrics.
Any other students must email sgpe@ed.ac.uk in advance to request permission. |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
|
| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
The assessment for this course consists of a 20% individual project and an 80% written exam. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understanding of financial data: Participants will acquire a deep understanding of various types of financial data, including returns, indices, and their stylized facts, enabling them to summarize, visualise, and interpret financial information e¿ectively.
- Quantitative analysis techniques: Participants will develop proficiency in quantitative analysis techniques such as linear regression, instrumental variable modelling, and maximum likelihood estimation, allowing them to evaluate risk factors and market e¿iciency.
- Financial time series analysis skills: Participants will gain experience in analysing stationary and non-stationary dynamics in financial time series, including singleequation dynamic models and vector autoregressive models, facilitating the identification of trends, mean reversion, and structural breaks in financial data.
- Risk modelling and forecasting techniques: Participants will learn risk modelling techniques such as value at risk estimation, portfolio optimisation, volatility modelling, as well as forecasting methodologies for financial time series, including point, interval, and density forecasting, and stochastic simulation.
- Critical thinking and problem-solving skills: Through various applications and case studies, participants will enhance their critical thinking and problem-solving skills, enabling them to critically evaluate financial theories, conduct empirical analysis, and make informed decisions in real-world financial scenarios.
|
Reading List
Main textbook:
Hurn, S., Martin, V. L., Yu, J., & Phillips, P. C. B. (2020). Financial econometric modeling. Oxford University Press.
Supplementary reading:
Dixon, M. F., Halperin, I., & Bilokon, P. (2020). Machine learning in finance (Vol. 1170). New York, NY, USA: Springer International Publishing.
Articles (list may be updated during the course, depending on the material covered):
Aït-Sahalia, Y., & Jacod, J. (2012). Analyzing the spectrum of asset returns: Jump and volatility components in high frequency data. Journal of Economic Literature, 50(4), 1007-1050.
Aldrich, J. (1997). RA Fisher and the making of maximum likelihood 1912-1922. Statistical science, 12(3), 162-176.
Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001). The distribution of realized exchange rate volatility. Journal of the American statistical association, 96(453), 42-55.
Barndorff-Nielsen, O. E., Hansen, P. R., Lunde, A., & Shephard, N. (2008). Designing realized kernels to measure the ex post variation of equity prices in the presence of noise. Econometrica, 76(6), 1481-1536.
Bollerslev, T., Patton, A. J., & Quaedvlieg, R. (2016). Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics, 192(1), 1-18.
Campbell, J. Y., & Yogo, M. (2006). Efficient tests of stock return predictability. Journal of financial economics, 81(1), 27-60.
Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.
Elliott, G., & Timmermann, A. (2008). Economic forecasting. Journal of Economic Literature, 46(1), 3-56.
Engle, R. F. (2000). The econometrics of ultra-high-frequency data. Econometrica, 68(1), 1-22.
Ghysels, E., Santa-Clara, P., & Valkanov, R. (2005). There is a risk-return trade-off after all. Journal of financial economics, 76(3), 509-548.
Liu, L. Y., Patton, A. J., & Sheppard, K. (2015). Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. Journal of Econometrics, 187(1), 293-311.
Malmendier, U., & Nagel, S. (2011). Depression babies: Do macroeconomic experiences affect risk taking?. The quarterly journal of economics, 126(1), 373-416.
Zhang, L., Mykland, P. A., & Aït-Sahalia, Y. (2005). A tale of two time scales: Determining integrated volatility with noisy high-frequency data. Journal of the American Statistical Association, 100(472), 1394-1411. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Yuejun Zhao
Tel:
Email: yuejun.zhao@ed.ac.uk |
Course secretary | |
|
|
University Homepage