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The University of Southampton

MANG6299 Quantitative Finance

Module Overview

The purpose of this module is to provide you with the necessary skills to undertake quantitative research in finance. In particular, we focus on analysing financial markets and firms’ investment and financing decisions. Lectures will introduce a broad range of topics (e.g. ARCH/GARCH). However, you will discover that by understanding and applying some basic concepts various issues can be analysed in a similar manner. In particular, we will introduce basic theoretical concepts developed in statistics and econometrics. Understanding the main theoretical methods is essential to appreciate the analytical tools and their applications to finance. The module is a compulsory module on the MSc Finance. The module introduces empirical methods used in finance and is a prerequisite for Advanced Time Series Modelling in the 2nd semester. In particular, cross-sectional, panel and time series methods are introduced and applied to financial data. The module will introduce methods developed in econometrics and apply these methods to financial data. The module will stress the relationship between finance, econometrics and statistics. The module will only be offered on the MSc Finance. The module provides an introduction to time series modelling, which will be extended in the optional module Advance Time Series Modelling (MANG6297).

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • how to demonstrate understanding of the basic theory of financial econometrics;
  • how to demonstrate understanding of some specific applications of such theory;
  • how to apply such understanding to a specific empirical project;
  • how to demonstrate competence in using an econometrics software package (Eviews).
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • demonstrate quantitative skills in evaluating numerical data.
Transferable and Generic Skills

Having successfully completed this module you will be able to:

  • demonstrate skills in utilising analysis software.


The module offers a comprehensive introduction to Quantitative Finance. Students will learn various methods to analyse financial data. Structure of linear models • Functional forms • Log linear models • Quadratic models • Lags • Dummy variables Exploring data • Descriptive statistics • Outliers • Missing data Regression analysis • Ordinary least squares (OLS) estimator • Classical OLS assumption • Statistical significance • Specification tests Testing for violation of assumptions and their implications • Heteroskedasticity • Autocorrelation • Multicollinearity • Endogeneity Analysing panel data • Fixed effects • Random effects • Serial correlation Limited dependent variable models • Logit and probit • Interpretation of analysis and marginal effects • Predictive analysis Time series modelling • Deterministic models • Stationarity and unit roots • ARIMA • Co-integration

Learning and Teaching

Teaching and learning methods

Teaching methods include: Weekly lectures will provide an overview of the main issues arising in the unit, and will be supplemented by weekly empirical and theoretical exercises. Exercises will support student learning by providing opportunities for students to attempt, and gain feedback on, numerical and problem-solving exercises. Students will also have the opportunity for both directed and non-directed independent reading The module will be taught by a mixture of methods ranging from guided background reading, lectures, group work and the exploration of mini case studies and datasets. The lecturer will draw upon market developments current at the time of the course. The lecturer will introduce the concepts, and participants will have the opportunity to practice and apply the methods discussed. We will do a step-by-step analysis of different financial data (stock market data, firm and industry-specific data). Learning activities include: • Eviews based exercises in class • A group assignment • Discussion of findings in class

Independent Study126
Total study time150

Resources & Reading list

Greene, W.H. (2000). Econometric Analysis. 

Campbell, J.Y., Lo, A.W. & A.C. MacKinlay (1997). The Econometrics of Financial Markets. 

Hayashi, F. (2000). Econometrics. 

Journal of Finance. Journal

Journal of Financial and Quantitative Analysis. Journal

Brooks, C (2014). Introductory Econometrics for Finance. 

Journal of Financial Economics. Journal

Chiang, A.C. (1984). Fundamental Methods of Mathematical Economics. 


Assessment Strategy

The assessment is aligned with the learning outcomes and objectives. The students will analyse financial data and will develop statistical models in STATA. They will apply the methods and concepts discussed in the lecture to real cases and various datasets. This will include the assessment of financial statements, stock market data, quantitative modelling in STATA and discussing management implications. The module will focus more on the practical aspects of time series modelling and will highlight applications in practice.


Computer practicals


MethodPercentage contribution
Individual Coursework 100%


MethodPercentage contribution
Individual Coursework 100%


MethodPercentage contribution
Individual Coursework 100%

Repeat Information

Repeat type: Internal & External


Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:


Recommended texts for this module may be available in limited supply in the University Library and students may wish to purchase the mandatory/additional reading text as appropriate. Core Text: £46 Student Eviews: £30

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at

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