Quantitative Finance | MANG6482 | University of Southampton

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 apply such understanding to a specific empirical project;
how to demonstrate understanding of some specific applications of such theory;
how to demonstrate competence in using an econometrics software package (Eviews).

Transferable and Generic Skills

Having successfully completed this module you will be able to:

demonstrate skills in utilising analysis software.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

demonstrate quantitative skills in evaluating numerical data.

Syllabus

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

Study time
Type	Hours
Teaching	24
Independent Study	126
Total study time	150

Resources & Reading list

General Resources

Journal of Financial Economics. Journal

Journal of Finance. Journal

Journal of Financial and Quantitative Analysis. Journal

Textbooks

Hayashi, F. (2000). Econometrics. Princeton: Princeton University Press.

Greene, W.H. (2000). Econometric Analysis. New York: Prentice Hall International Inc..

Chiang, A.C. (1984). Fundamental Methods of Mathematical Economics. Singapore: McGraw-Hill.

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

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

Assessment

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.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Computer practicals

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Repeat Information

Repeat type: Internal & External