Programme content
The MSc in Quantitative Finance is made up of eight core modules, three optional modules and culminates in a dissertation.
Term 1: October to December
All students must take five core modules:
Financial Stochastic Processes (15 credits)
Due to their inherent randomness, it is natural to model financial and economic systems using probability models and stochastic processes. Analysis of appropriate stochastic models has become extremely important in recent years, such as for accurately pricing options. This module gives a thorough introduction to stochastic processes in general and their use in modelling in business, finance and economic applications. Students will gain understanding about how both simulation and mathematical techniques can be used to learn about stochastic processes. [more information]
Statistical Methods for Financial and Economic Applications (10 credits)
There are substantial amounts of data collected relating to business, financial or economic applications. Examples include data on stock returns and survey data used for credit-scoring. This module will cover how such data can be modelled, how inferences about the models can be made and how statistical models can be used for predicting future outcomes and behaviour. [more information]
Spreadsheet Modelling for Quantitative Finance (5 credits)
This course provides students with basic spreadsheet modelling skills. At the end of this module students will be able to use simple Monte-Carlo simulation, simple optimisation using solver, and to understand the structure of VBA programmes, Pivot Table and user interface development. [more information]
Financial Markets (15 credits)
This course provides students with the necessary theoretical and technical skills to value and use various financial instruments, such as forwards, futures, options and swaps. [more information]
Optimisation (10 credits)
Optimisation is concerned with maximising or minimising a mathematical function of some decision variables, subject possibly to various constraints. It has numerous applications, not only in Quantitative Finance, but in many other fields. This module aims to provide a foundation in key optimisation methods, and to illustrate their strengths and weaknesses as tools for modelling and solving real-world problems. Techniques covered include linear programming, nonlinear programming, integer programming and stochastic programming. Throughout, examples are given of applications in Quantitative Finance, Statistics and Operational Research. [more information]
Term 2: January to April
All students take three core modules:
Derivatives Pricing (15 credits)
This course covers the methods used in the valuation of a range of complex derivative securities. [more information]
Economics for Money, Banking and Finance (10 credits)
The first part of this module looks at the advanced microeconomic theory of banking, including topics such as competition in banking, risk analysis and credit market imperfections. The second part focuses on the macroeconomics of money and banking, including policy debates about monetary and fiscal stabilisation, the critique of policy formulation, time inconsistency and the rationale for an independent central bank. [more information]
C++ Programming for Quantitative Finance (10 credits)
C++ has become very popular in quantitative finance. Many employers expect employees to have a good knowledge of object-oriented programming using C++. This module provides students with a strong foundation in object-oriented programming using C++ and will enable them to improve their programming skills independently. At the end of this module, students should be able to develop quantitative finance applications such as pricing and hedging models. [more information]
Students also take three of the following optional modules:
Assessing Financial Risk: Extreme Value Methods (10 credits)
Assessment of financial risk requires accurate estimates of the probability of rare events. Estimating the probability of such “extreme” events is challenging, as by nature they are sufficiently rare that there is little direct empirical evidence on which to base inference. Instead we have to extrapolate based on the past frequency of the occurrence of less extreme events. This module covers ideas from Extreme Value Theory, which give a sound mathematical basis to such extrapolation, and shows practically how it can be used to give accurate assessments of financial risk in a wide range of scenarios. Reducing the risk of a share portfolio may be possible through choosing shares from companies in a wide range of sectors. This module will also cover the theory for multivariate extremes. [more information]
Behavioural Finance (10 credits)
This course extends the analytical tools used for evaluating strategic and investment decisions learnt in other modules by deviating from the paradigm of rational decision making. The module focuses on the implications of investor behaviour and capital market imperfections (such as limits to arbitrage) for investment management. The concepts of this module are a foundation for value investing, arbitrage, asset management and opportunistic corporate finance. The module uses insights from psychology and behavioural finance to complement traditional market frictions and explain the behaviour of capital markets. [more information]
Business Forecasting (10 credits)
This course introduces time series and causal forecasting methods so that students can prepare methodologically competent, understandable and concisely presented reports for clients. [more information]
Financial Econometrics (10 credits)
This course examines the statistical techniques employed in financial time series. [more information]
International Banking and Risk Management (10 credits)
This course examines the economics of financial intermediation and disintermediation, with some emphasis on the UK as a major international financial centre. Covering commercial and investment banking, the module examines practical issues of risk management by intermediaries as well as the potential risks involved in the more recent trend towards financial disintermediation. Regulatory issues are addressed, with attention paid to both ‘on’ and ‘off’ balance sheet positions. [more information]
International Money and Finance (10 credits)
This course explains the nature and relevance of derivative instruments for hedging purposes in the currencies market, and focuses on the international analysis of risk through the extension of basic interest relationships, including futures rates, swaps and investment returns. The focus is on approaches to measuring contractual and operational exposure and on understanding the relevance of financial information for the development of exchange rate risk hedging. [more information]
Data Mining for Marketing, Sales and Finance (10 credits)
At the heart of many real management problems are increasingly large data sets that need to be analysed efficiently and soundly. Often the aim of the analysis is to better understand some aspect of an organisation’s activities, and to draw out insights, or perhaps models, to enable managers to make better informed decisions and perhaps to predict the consequences of the decisions they are considering. This sort of analysis is often described as ‘Data Mining’, and can be approached using a range of statistical methods and novel methods from machine learning and computational intelligence, including decision trees, artificial neural networks, support vector machines etc. This module considers a number of multivariate models that are useful in interpreting the type of complex data bases that are increasingly common in modern business. In particular, we will consider data mining methods for exploration and data reduction, that aim to simplify and add insights to large, complex data sets, and data mining methods for modelling, that aim to classify individuals into distinct, disjoint segments with different patterns of behaviour. Based upon the knowledge of data mining algorithms, the course will develop the skills of actively conducting business data mining using SAS Enterprise Miner on a number of empirical datasets and applications from customer relationship management, (e.g. churn prediction), direct marketing (e.g. cross-selling or up-selling of products or services), and finance (e.g. credit scoring for mortgages and credit cards). [more information]
Term 3: May to September
The final element of the MSc is the dissertation, a substantial piece of independent work conducted over the summer months through to September. The dissertation gives students the opportunity to apply research techniques and relevant theory to a research topic. Students can choose a dissertation topic from any one of the four participating departments: Accounting and Finance; Economics; Management Science; or Mathematics and Statistics. [more information]
