FINN2031: Financial Econometrics 1

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FINN2031: Financial Econometrics 1

Aims

• To develop knowledge and understanding of key issues and concepts in Financial Econometrics.
• To equip students with the skills required to appreciate the applied literature in finance, to undertake an applied financial econometrics project and to prepare students for Financial Econometrics at higher levels.
• To offer the opportunity to develop key skills.

Content

• Review of Statistics: random variables, probability distributions and statistical inference.
• Simple linear regression model: OLS, its assumptions and properties (BLUE, GM Theorem, t-test, etc).
• Multiple regression model: assumptions, statistical inference (F-test), goodness of fit).
• A consideration of the breakdown of some of the OLS assumptions: autocorrelation, heteroscedasticity and model mis-specification.
• Simultaneous equation model and endogeneity: causes of endogeneity, 2SLS, IV, and Hausman test).
• Panel data analysis (within and between estimators, fixed effects, diff-in-diff, random effects).

Learning Outcomes

• Have knowledge and understanding of the key theoretical and practical issues in Financial Econometrics.
• Have knowledge of the underpinning mathematical construction of an econometric model.
• Have knowledge of the assumptions and how these assumptions drive the estimation and diagnostic testing of an econometric model.
• Have knowledge of how to obtain and manipulate data in preparation for an econometric analysis.

• Be able to apply econometric methods to the estimation of financial relationships and to interpret the results.
• Be able to undertake an applied financial econometrics projects (estimating relationships using an econometric software package).
• Prepare students for the study of Financial Econometrics at higher levels.
• Critically evaluate contemporary literature that uses econometric techniques either covered in the module or proximate to those covered in the module.
• Evaluate the appropriateness of a particular econometric technique for a specific financial application.

• Written Communication - by completing the summative assignment.
• Planning, Organisation and Time Management.
• Problem Solving and Analysis - by applying the necessary analytical and quantitative skills to identify and empirically test theoretical relationships.
• Initiative - by collecting information for the summative assignment, searching relevant literature and information in preparation for the summative assignment.
• Numeracy - by applying an array of core mathematical-statistical skills;
• Computer Literacy and Information Retrieval - by word-processing the summative assignment and estimating relationships using an econometric software package.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures provide an introduction to the key theoretical and empirical issues.
• Computer Laboratories introduce students to econometric software used in preparation of the summative assignment.
• Formative assessment is by means of an assignment and a multiple choice test.
• Summative assessment is by means of a written assignment.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

One written assignment of 1500 words and a time constrained multiple-choice question test.