2023/24 Undergraduate Module Catalogue
LUBS2227 Financial Econometrics
10 creditsClass Size: 186
Module manager: Vladimir Pazitka
Email: V.Pazitka@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2023/24
This module is mutually exclusive with
LUBS2575 | Statistics and Econometrics |
Module replaces
LUBS2224 Credit and Financial AnalyticsThis module is not approved as a discovery module
Module summary
This module is intended to equip students with analytical skills and theoretical knowledge necessary to apply econometric techniques to the analysis of financial datasets. Students will get hands on experience working with econometric software, managing and analysing a variety of financial datasets. The skills and knowledge that students will develop can then be applied in other finance modules both in year 2 and final year.Objectives
The module aims to provide students with a basic, through to more advanced, level of understanding of econometric techniques as applied in finance. Students will be equipped with the tools required to analyse financial datasets and to test theories and hypotheses in this field. Students will be given computer-based tasks in addition to class tasks and are expected to develop competence in the use of statistical and econometric software for data management and econometric analysis.Learning outcomes
On completion of the module students will be able to:
- Demonstrate an advanced level of knowledge of econometric tools employed in finance, incorporating knowledge from the CFA syllabus in quantitative methods;
- Competently use econometric software to conduct empirical investigations and interpret the output;
- Identify and critically evaluate how financial econometrics is used in current applied literature;
- Critically evaluate published empirical papers in finance journals.
Skills outcomes
On completion of this module students will be able to:
- Demonstrate advanced problem solving, analytical and quantitative skills by applying current theory and appropriate analytical tools to complex problems in finance;
- Competently use econometric software to conduct empirical investigations and interpret the output;
- Identify and critically evaluate how financial econometrics is used in current applied literature;
- Critically evaluate published empirical papers in finance journals.
Syllabus
Indicative content:
- Introduction to econometrics and econometric software
- Working with financial data
- Probability distributions and hypotheses testing
- Classical linear regression model
- Multiple linear regression model
- Model validity and diagnostics
- Empirical studies in finance
Teaching methods
Delivery type | Number | Length hours | Student hours |
Workshop | 9 | 2.00 | 18.00 |
Lecture | 10 | 1.00 | 10.00 |
Independent online learning hours | 20.00 | ||
Private study hours | 52.00 | ||
Total Contact hours | 28.00 | ||
Total hours (100hr per 10 credits) | 100.00 |
Private study
This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.Opportunities for Formative Feedback
Students will have the opportunity to take a practice exam and will receive a formative feedback on it. Formative feedback will be also provided on exercises covered in workshops and through weekly quizzes.Methods of assessment
Coursework
Assessment type | Notes | % of formal assessment |
Report | 1,000 word individual project report | 50.00 |
Total percentage (Assessment Coursework) | 50.00 |
The resit for this module will be 100% by 2,000 word coursework.
Exams
Exam type | Exam duration | % of formal assessment |
Standard exam (closed essays, MCQs etc) | 1 hr 00 mins | 50.00 |
Total percentage (Assessment Exams) | 50.00 |
.
Reading list
The reading list is available from the Library websiteLast updated: 05/05/2023
Browse Other Catalogues
- Undergraduate module catalogue
- Taught Postgraduate module catalogue
- Undergraduate programme catalogue
- Taught Postgraduate programme catalogue
Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD