EXAM CODE (ES50061/ES50109)
University of Bath

DEPARTMENT OF ECONOMICS

EXAMINATION CODE (ES50061 – ES50109)

FINANCIAL ECONOMETRICS



05/05/2021 (09:30am) – 08/05/2021 (09:30am)




INSTRUCTIONS – PLEASE READ UNTIL THE END

Use the file StudentAssessmentFrontPageCW.doc as your answer file.

Please fill in the student and unit details on page one in
StudentAssessmentFrontPageCW.doc

If not further specified, all references in addition to the lecture material must be detailed in a
reference list.

If not further specified, any word count applies to the main body of the answer, i.e. reference,
tables, figures, and formulas are excluded.

Submit your answers as a single PDF file.

By submitting your answer file, you agree to the terms outlined in the declaration in
StudentAssessmentFrontPageCW.docx


BEFORE YOU SUBMIT YOUR ANSWERS ON MOODLE:
• Make sure that your first page contains your candidate number & the unit details.
• Save your answer in PDF format using the following name:
CandidateNumberUnitCode.pdf













EXAM CODE (ES50061/ES50109)
Please answer both questions.
Each question caries 50 marks.
Everything except equations or hand-drawn graphs must be typed.
There is no strict maximum wordcount. Around 2000 words for each question is
advised. Include the wordcount at the start of each answer.
Submit your answers in an 11pt font, 1-line spacing pdf document with normal
margins (as defined in MS Word Layout tab).
Referencing is not mandatory but if you include references they must be in academic
style and include only journal articles.


Question 1. Present and contrast the Vector Autoregression (VAR) and Vector Error
Correction (VECM) models. In your discussion, elaborate on the situations in which each of
these models is appropriate and present in detail how you would test a data set to decide
which of these models to use.

Question 2. Many financial time series are characterized by dynamic behavior in the variance
(ARCH effect). Describe a testing and modelling methodology that can take this feature of
financial data into account. Further, discuss how models such as the T-GARCH, E-GARCH
and GARCH-M are designed to address particular characteristics of Financial data and how
this is accomplished by each of these models.















































































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