Postgraduate Course: Research Methods in Carbon Finance (CMSE11205)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | This course aims to turn out students who are able to approach dissertations with all the necessary research methods training to address most carbon finance issues as well as enter any organisation and have the skills and knowledge on the key areas to research and evaluate carbon finance. |
| Course description |
This course has been created specifically for the MSc in Carbon Finance, reflecting the cross-disciplinary nature of the programme, as well as the niche aspects of Carbon Finance as an emerging discipline. Carbon Finance is a rapidly growing niche field which requires an understanding of statistics/econometrics as well as an understanding of the unique nature of carbon trading and investments. In addition to special focus on carbon financial analysis, this course will provide you with applications and motivations for statistical/econometrics model building in modern finance as a whole. The course content in this regard is similar to an undergraduate statistics course and will also be backed up by practical demonstration on an econometrics software package; hence the material should be accessible for both students with strong quantitative backgrounds and those who do not have this background but are willing to put an appreciable level of effort into learning the class material. The skills developed on this course are transferable and may also be very useful for your dissertation.
Syllabus
Qualitative Research Methods
Basic Data Handling
Working with data
Introduction to Probability
Sampling and sampling distributions
Statistical Inference: Hypothesis Testing for Single Populations
Statistical Inference: Hypothesis testing about two populations
Correlation
Classical linear regression model
Dummy variables
CLRM Assumptions and Diagnostics
Modelling long run relationships in finance
Student Learning Experience
Statistics like other branches of applied mathematics is best learnt through individual application. Students should endeavour to study recommended materials in advance of a lecture as well as after the lecture if still unclear. Attendance of lectures does not guarantee the understanding of concepts. Reading materials should also be studied in advance of the lab sessions.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2017/18, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
150
(
Lecture Hours 20,
Seminar/Tutorial Hours 4,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
121 )
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| Additional Information (Learning and Teaching) |
Prep reading 40; Prep for exam 41; Coursework 40.
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
40 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Individual Assessment worth 40%
Final exam worth 60%
Form of Assessment:
An examination counts for 60% of the grade. The remaining 40% is based on an econometrics-based assessment.
Assessment Criteria:
The exam and other assessments will be assessed using the University¿s Postgraduate Common Marking Scheme, as detailed in the Code of Practice for Taught Postgraduate Programmes.
Dates of Assessment:
Assessment/coursework ¿ Due Monday 11th April 2016 at 2pm |
| Feedback |
Summative marks will be returned on a published timetable, which has been made clear to students at the start of the academic year.
Feedback will comprise students interpreting concepts during lab sessions, coursework Feedback and generic examination Feedback. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Identify, critically evaluate, select, justify and apply appropriate research methods to relevant research questions, in order to ensure that the evidence generated, its analysis and the conclusions drawn from it are valid and reliable.
- Present the findings of research in an academic manner.
- Define, critically evaluate and apply the major tools used by financial economists (correlation, regression and time series analysis) =.
- Link statistical and econometrics theory with empirical applications.
- Conduct empirical analysis with econometrics software packages such as EViews 8.
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Reading List
Koop, G. (2006) Analysis of Financial Data, John Wiley & Sons Ltd: Chichester. Accessible text for students new to statistics/econometrics.
Brooks, C. (2008) Introductory Econometrics for Finance, Cambridge University Press: Cambridge. Relevant text for financial econometrics.
Cortinhas, C. and Black, K. (2012) Statistics for Business and Economics, John Wiley & Sons Ltd: Chichester.
Booth, W. C., Colomb G. G. and Williams, J. M. (2008) The Craft of Research, 3rd Edition, University of Chicago Press.
Business Research Methods, Alan Bryman and Emma Bell, 2nd Edition. This is a text book which covers many research methods used in business and is used in many business schools as the standard research method text. |
Additional Information
| Graduate Attributes and Skills |
Cognitive Skills:
After completing this course, students should be able to:
- Develop analytical, numerical and problem solving skills
- Critically assess existing understanding in a defined area of knowledge;
- Recognize qualitative and quantitative techniques appropriate to the analysis of particular circumstances;
- Apply a range of relevant qualitative and quantitative research methods;
- Use relevant literature and data reference materials.
Subject Specific Skills:
After completing this course, students should be able to:
- Understand and use statistics notations and theory to solve a wide range of problems in Finance and particularly Carbon Finance
- Understand various research approaches which they can apply to their dissertation projects
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Gbenga Ibikunle
Tel: (0131 6)51 5186
Email: |
Course secretary | Miss Ashley Harper
Tel: (0131 6)51 5671
Email: |
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