SOAS University of London

School of Finance and Management

Econometric Principles and Data Analysis

Module Code:
15PFMC096
Credits:
15
FHEQ Level:
7
Year of study:
Year 1
Taught in:
Term 1

Econometric Principles and Data Analysis will be renamed Financial Modelling Techniques for the 2020/21 session.

This module teaches students a wide range of techniques for summarising data, estimating probabilities, analysing data and testing hypotheses. The module has three sections.

In the first section, students will learn the techniques for calculating measures of central tendency and dispersion of a dataset, such as mode, median, mean, range, standard deviation, skewness and kurtosis.

In the second section, students will learn how to use some probability models to estimate the likelihood of occurrence of some events that a finance manager may be interested in. Models that will be considered will include the normal distribution, binomial distribution, multinomial distribution, uniform distribution, exponential distribution and Bayes formula.

In the final section, students will learn parametric techniques for analysing data, constructing financial models and testing a hypothesis.

Topics that will be covered will include the procedure for testing a hypothesis, confidence intervals, t-test, F-test, z-test, simple regression, multiple regression, VAR, ARCH and GARCH techniques as well as the strengths and limitations of each technique.

Objectives and learning outcomes of the module

On successful completion of this course a student will be able to:

  • Demonstrate understanding of descriptive analysis
  • Explain the principles of regression analysis, with applications to finance
  • Produce and interpret plots of data, as applied to finance problems
  • Use the program Eviews to estimate a regression equation, and interpret the results, for bivariate (two-variable) regression models and multiple regression models, drawing from finance theory
  • Discuss the tests used to identify correct model specification, and statistical criteria for choosing between models, with applications to finance
  • Use Eviews to conduct tests for heteroscedasticity, correlated disturbances, non-normal disturbances, functional form, and model selection, with applications to finance

Workload

Lectures

This module consists of a 2-hour weekly lecture over 10 weeks of term plus a revision lecture in term 3 as preparation for the final examination. Students will be supplied with a syllabus with a breakdown week by week of required and additional reading. Reading materials are usually accessed electronically from the BLE and students should come to class prepared.

Tutorials

This module also has a weekly 1-hour tutorial where the questions posed by the tutor relevant to the lecture are explored and discussed by the students. Students also prepare and deliver a short presentation.

Total Work load:

Students on this module will have 3 taught hours each week. Additionally, adequate personal study time should be allocated for reading and class preparation.

Scope and syllabus

  1. Preliminary and descriptive Statistics
  2. Probability as applied to finance
  3. Testing finance theories and discuss the strengths and limitations of a wide range of test techniques;
  4. apply classical linear regression techniques;
  5. VaR, ARCH and GARCH techniques;
  6. Unit root and cointegration in long run relationships in finance;
  7. Non-parametric tests and know the strengths and limitations of the tests techniques.

Method of assessment

Assessment for this module is in three elements:

  1. One tutorial presentation at 10%
  2. One essay of 2,500 words at 30%
  3. One unseen 2-hour written examination at 60%

All elements except the presentation may be resubmitted.

Disclaimer

Important notice regarding changes to programmes and modules