Module Code: H9FE
Long Title Fundamentals of Financial Econometrics
Title Fundamentals of Financial Econometrics
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: COLETTE DARCY
Module Author: Isabela Da Silva
Departments: School of Business
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Integrate and apply theoretical and practical knowledge of the core concepts and techniques in econometrics, with specific focus upon the classical linear regression model and the underlying statistical principles and concepts of regression analysis
LO2 Conduct independent econometric and statistical analysis of financial and economic data using the method and principles of the Ordinary Least Square criterion within the context of simple linear and multiple regression models.
LO3 Apply appropriate statistical testing of the underlying assumptions of linear regression analysis and hence reflect critically upon the model adequacy and correct for any regression disturbance issues (e.g. autocorrelation and heteroscedasticity).
LO4 Critically evaluate the tools of econometrics to estimation, inference and forecasting in the context of real world economic and financial problems.
LO5 Demonstrate proficiency in the use and application of software to conduct econometric analysis using real world financial and economic datasets and hence critique and interpret reported results in a technical and non-technical manner.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

There are no additional entry requirements for this module.  The programme entry requirements apply. 
 

Module Content & Assessment

Indicative Content
Introduction
The Role of Econometrics in Financial and Economic Analysis Nature and Sources of Data for Economic/Financial Analysis Variable Types and Scales Regression versus Correlation
Simple Linear Regression Model
Linear Regression: Principles of Ordinary Least Squares Technique (OLS) The Classical Linear Regression Model: The Assumptions Underlying Regression Analysis Standard Error of Least Squares Estimates Using Regression for Predictions Testing Significance of Regression Coefficients
Multiple Regression Model
Assumptions underlying Multiple Regression Approaches to Building the Multiple Regression model Multiple Regression Analysis: Obtaining the OLS estimates R2 and Adjusted R2
Hypothesis Testing in Multiple Regression
Testing the Regression Coefficients Testing the Significance of the Multiple Regression Model: The F Test R2 versus F Test
Regression Model Violations and Diagnostic Tests
Issues in Regression Analysis Hetroskedasticity Autocorrelation Causes and Consequences Hetroskedasticity Autocorrelation Testing the Multiple Regression Model Hetroskedasticity (The Breusch Pagan Godfrey Test) Autocorrelation (Durbin Watson Test)
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 40
Assessment Date: n/a Outcome addressed: 2,3,5
Non-Marked: No
Assessment Description:
The continuous assessment will be empirical in nature and may take the form of a large-scale data-based project with prescribed tasks; a technical report or a detailed problem set based assignment which may contain case study data. Presentations may also be used in conjunction with any of the aforementioned assessment methods where appropriate. The continuous assessment element of this module will assess both theoretical and analytical skills as undertaken in the module and candidates must demonstrate skills of analysis and interpretation of data regardless of the form of assessment. Depending on the scale and nature of the task, this may take the form of a group assessment. An example of such an assessment would be where learners are asked to formulate an appropriate financial or economics led research question which requires the analysis of data. For example, testing a theory in finance and/or analysing economic behaviour. Learners are expected to: select and collect real-world data and hence undertake econometric analysis using appropriate software to answer the research question. Learners will be expected to critically evaluate empirical results, considering issues of model specification, assumption violation etc.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
The examination will be a minimum of two hours in duration and may include a mix of: short or long problem-based questions, vignettes, essay-based questions, and case study-based questions. All questions will be marked according to clarity, structure, contemporary examples (that illustrate points made), reference to materials covered, theories and research in the field. Reference to class material and evidence of outside reading is essential
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
Repeat assessment of this module will consist of a repeat examination which will test all the learning outcomes.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and demonstrations 24 Per Semester 2.00
Directed Learning Directed e-learning 24 Per Semester 2.00
Independent Learning Independent learning 77 Per Semester 6.42
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Gujarati, D.N., (2021). Essentials of Econometrics. SAGE Publications.
Supplementary Book Resources
  • Wooldridge, J.M., (2020). Introductory Econometrics: A Modern Approach, 7th edt., Cengage Learning.
  • Stock, J.H. and Watson, M.W., (2020). Introduction to Econometrics, 4th ed , Pearson Publications.
  • DeFusco, R.A., McLeavey, D.W., Pinto, J.E, Runkle, D.E. and Anson, M.J, (2020). Quantitative Investment Analysis. Wiley Publications (Chartered Financial Analyst ™) (e-book available).
  • Koop, G. (2013), Analysis of Economic Data, Wiley Publications.
  • Davison, M. (2014), Quantitative Finance: A Simulation Based Introduction Using Excel, Chapman and Hall/CRC.
  • Rivera, R, (2020), Principles of Managerial Statistics and Data Science, Wiley Publications.
This module does not have any article/paper resources
Other Resources
Discussion Note: