ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Financial Econometrics
Code ACFI901
Coordinator Dr M Stamatogiannis
Finance and Accounting
M.Stamatogiannis@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 20

 5

         25
Timetable (if known)              
Private Study 125
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Examination Standard UoL penalties apply Marked anonymously There is a resit opportunity  2    80       
Mid-term test Standard UoL penalties apply Marked anonymously There is a resit opportunity  1    20       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             

Aims

The aim of this module is to provide students with a solid foundation in the statistical and econometric techniques that allow them to conduct independent empirical investigations in economics and finance. The approach centres on the linear multiple regression methods, including their use in estimating and testing the validity of models in economics and finance. The aims are that students will:

Understand aspects of the theories and principles of econometric analysis in economics and finance;

Be aware of a range of inferential techniques commonly employed in econometrics;

Understand the limitations of such techniques in different circumstances.

Learning Outcomes

(LO1) Students will be able to discuss, evaluate and apply a range of mathematical and statistical techniques necessary for understanding and using econometrics methodology in finance and economic development.

(LO2) Students will be able to use a number of statistical and econometric packages in real (simple) applications.

(LO3) Students will be able to formulate, estimate and test a wide range of linear and non-linear models commonly encountered in financial analysis.

(S1) Problem solving
Students will be required to develop problem solving skills in lectures and practical lab sessions.

(S2) Numeracy
Numeracy skills will be developed through the application of techniques taught in lectures to various real and artificial data sets.

(S3) IT skills
IT skills will be developed through the application of techniques taught in lectures to various real and artificial data sets.

(S4) Communication skills
Communication skills, essential in econometrics, will be developed through the careful interpretation and guided discussion of results in practical sessions.

(S5) Time management
Time management and management of learning skills will be developed in part by working to a midterm exam while acquiring the skills as they go, and further by working towards passing the final exam.

Teaching and Learning Strategies

2 hour lecture x 10 weeks
1 hour seminar x 5 weeks
125 hours self-directed learning

Syllabus
 

Introduction of quantitative research techniques;

Reviews of probability and statistics;

Least squares estimation;

Inference and hypothesis testing;

Specification and functional form;

Heteroskedasticity;

Autocorrelation;

Introductory time-series analysis;

Simulation analysis;

Technical analysis;

Finance applications and examples;

Software and data sources: Eviews, Bloomberg, Datastream etc.

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.