Introductory description

This module is designed to provide an introduction to advanced quantitative methods to students with quantitative backgrounds.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1.Review of Math concepts: Differentiation and Optimisation.
2.Basics of Probability Theory.
3.Basics of Statistics. Populations and Samples.
4.Hypothesis testing. Basic statistical tests.
5.Linear regression model. Estimation and Interpretation.
6.Linear regression model. Inference. Basis Extensions of the linear regression.
7.Panel regression.
8.Time series modelling.
9.Review.

Learning outcomes

By the end of the module, students should be able to:

• Describe a data set in a way that highlights what is important, by drawing the client directly to relevant comparisons.
• Choose which statistical method is most appropriate for each kind of data, and each kind of research question.
• Demonstrate understanding of mathematical and statistical tools and techniques used in quantitative and computational finance.
• Demonstrate the ability to think quantitatively about data and determine what data can tell us, and what it cannot.

Indicative reading list

Gary Koop, Introduction to Econometrics, 2008, Wiley.
Wisniewski, M, Quantitative methods for decision makers, 2016, FT Prentice Hall.
Hill, Griffiths, Lim, Principles of Econometrics, 2008, Wiley.
Ruppert, David. Statistics and data analysis for financial engineering. New York, NY: Springer, 2011.

Subject specific skills

Conduct basic inferential statistical tests, such as t tests and Chi-square tests.
Conduct and interpret a multiple regression analysis.
Conduct and interpret a logistic regression analysis.
Use statistical package (such as R or Stata) to analyse financial data, estimate statistical models, and construct optimised portfolios.

Transferable skills

Develop facility with spreadsheets and statistical analysis packages.
Analyse real world data and solve real world problems.