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FIN41660

Academic Year 2024/2025

Financial Econometrics (FIN41660)

Subject:
Finance
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Alessia Paccagnini
Trimester:
Autumn
Mode of Delivery:
Not yet recorded
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This course aims to provide students with a broad understanding of the discipline of econometrics
for finance, from an ab initio vantage point. It endeavours to explain the nature of financial econometrics and to provide illustrative applications in business, economics and finance. In particular,
this course focuses on the classic linear regression model, the assumptions of that model and several
diagnostic tests to examine same. The course derives and discusses the Ordinary Least Squares
estimation technique. It also introduces alternative estimation methodologies, namely a number of
Least Squares variants, the Maximum Likelihood Estimator and the Generalized Method of Moments Estimator. Finally, several non-linear type regression models are considered.
The MATLAB computer software package will be used to estimate models and perform diagnostic tests.

About this Module

Learning Outcomes:

At the end of this course students should:
1) have knowledge as well as an understanding of the essential components of introductory and
intermediate level econometrics;
2) be able to apply econometric techniques to address important questions in business, economics
and finance;
3) have developed a "healthy" skepticism towards econometric models and their estimation based
on a sensible consideration of the data and techniques employed;
4) have had the opportunity to practice problem solving and improve their analytical skills.

Student Effort Hours:
Student Effort Type Hours
Lectures

90

Small Group

1

Tutorial

30

Total

121


Approaches to Teaching and Learning:
Students will be involved in active learning based on lectures' quizzes and applied projects.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
ECON42710 - Adv Metrics: Time Series


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: Practical Individual project by using coding language Week 12 Alternative linear conversion grade scale 40% Yes
35
Yes
Quizzes/Short Exercises: Quizzes during lectures to assess participation and understanding of topics Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12, Week 14, Week 15 Alternative linear conversion grade scale 40% Yes
10
Yes
Assignment(Including Essay): Final online project Week 12 Alternative linear conversion grade scale 40% Yes
20
Yes
Practical Skills Assessment: Mid-term test in-class Week 6 Alternative linear conversion grade scale 40% Yes
35
Yes

Carry forward of passed components
Not yet recorded
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Not yet recorded

Name Role
Ms Sinian Zheng Tutor
Mingchuan Zhou Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 7 Fri 13:30 - 15:20
Autumn Tutorial Offering 1 Week(s) - 8, 9, 10, 11, 12 Fri 14:30 - 15:20
Autumn Lecture Offering 1 Week(s) - 9, 10, 11, 12, 13 Mon 16:00 - 17:50
Autumn Lecture Offering 1 Week(s) - 7, 8, 9, 11, 12 Thurs 13:30 - 15:20
Autumn Exam Offering 1 Week(s) - 10 Thurs 16:00 - 17:50
Autumn Tutorial Offering 2 Week(s) - 8, 9, 10, 11, 12 Fri 13:30 - 14:20
Autumn Lecture Offering 2 Week(s) - 7 Fri 13:30 - 15:20
Autumn Lecture Offering 2 Week(s) - 9, 10, 11, 12, 13 Mon 13:30 - 15:20
Autumn Exam Offering 2 Week(s) - 10 Thurs 16:00 - 17:50
Autumn Lecture Offering 2 Week(s) - 7, 8, 9, 11, 12 Thurs 16:00 - 17:50