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BSc International Business, Finance and Economics with Industrial/Professional Experience / Course details
Year of entry: 2020
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Course unit details:
|Unit level||Level 3|
|Teaching period(s)||Semester 2|
|Available as a free choice unit?||Yes|
See course Blackboard pages.
|Unit title||Unit code||Requirement type||Description|
|Mathematical Economics I||ECON20120||Pre-Requisite||Compulsory|
|Time Series Econometrics||ECON30401||Co-Requisite||Compulsory|
The aim of this course unit is to establish the foundations and principles of modern financial econometrics providing students with appropriate econometric techniques for empirical investigation in finance and financial economics.
On completion of this unit you should be able to:
- Establish the specific characteristics of financial data.
- Undertake essential data handling tasks.
- Understand the features of univariate time series models.
- Understand the problem of volatility modelling and apply appropriate volatility modelling techniques.
- Model the relationship between multivariate variables.
- Undertake empirical analysis using econometric software (such as EViews).
- Use the acquired tools to read texts which introduce new methods.
One area in which time series econometrics is relied upon heavily is finance. Financial data are usually time series data and have rather unique statistical features. Importantly, they are also available in abundance. For instance you may have access to transaction data that give you second-by-second information. You will learn about the unique features of financial data and how high-frequency data can be used to model volatility and other aspects of return distributions.
At the core of financial econometrics is the issue of volatility modelling. It deserves its importance as volatility is frequently used as a proxy for risk. In that context, modelling volatility turns into modelling risk which has very obvious real life applications in finance. You will also learn about some alternative approaches to modelling risk, such as the celebrated conditional autoregressive value-at-risk model.
The multivariate versions of the volatility models allow the econometrician to estimate, and subsequently forecast, the correlation between several assets. This is a task of crucial importance as the value of correlations, to mention one application, determines the value of diversification.
This course unit will teach you the theoretical underpinnings of volatility and value-at-risk models as well as how to apply these using econometric software. Any student who is planning to apply their skills in the finance industry should consider choosing this course unit.
The course will cover the following topics.
- Review of basic concepts of probability and statistics and univariate time series modelling with applications to financial data.
- Univariate volatility modelling.
- High frequency prices and realized volatility.
- Forecast evaluation.
- Multivariate volatility models.
Students may consult the 2016/17 lecture notes available at sites.google.com/site/oryschenko/teaching to get a better idea of the course content. However, please be aware that the content and presentation of the material may change.
Teaching and learning methods
Lectures and exercise classes.
- Problem solving
- To identify, analyse, and solve a problem applying the most appropriate techniques, and to understand the limitations of the proposed solutions.
- To plan, conduct, and report on independent research.
- Written communication
- To develop an accurate and succinct argument and to communicate it in writing.
- To develop efficient time management skills. Numerical and computer literacy.
|Project output (not diss/n)||20%|
- Students will be given an opportunity to receive feedback on attempted exercises (7 problem sets) before and during the class in question.
- Students will receive formative feedback on the empirical project before the final exam.
- Students can also receive further feedback from classes, office hours, and revision sessions.
Main reading includes (selected chapters from):
- Chris Brooks (2014), Introductory Econometrics for Finance, 3rd ed., Cambridge University Press.
- Michael P. Clements and David F. Hendry (eds), A Companion to Economic Forecasting, Blackwell Publishing, 2004.
- Ruey S. Tsay (2013), An Introduction to Analysis of Financial Data With R, John Wiley & Sons.
and/or Ruey S. Tsay (2010), Analysis of Financial Time Series, 3rd ed., John Wiley & Sons.
|Scheduled activity hours|
|Assessment written exam||2|
|Practical classes & workshops||8|
|Independent study hours|
|Vitaliy Oryshchenko||Unit coordinator|