The module covers the building blocks of econometrics and analytical techniques used in finance. Via case studies and computer modelling exercises, students learn how to apply these techniques to real data. Emphasis is placed on practical applications of the techniques in the global financial markets.

Teaching Staff

Dr Chardin Wese Simen

This module provides an overview of the key building blocks in modern finance theory and introduces techniques for analysing and valuing different classes of risky assets such as equities and derivatives contracts. It also develops ways of optimally selecting portfolios of such assets and develops models of how these portfolios can be priced in financial markets. The techniques introduced in this module are widely applied in other elements of the programme. The module includes simulated trading sessions in our state of the art dealing rooms, where participants are introduced to real world pricing and trading strategies (INVEST sessions).

Teaching Staff

Dr Konstantina Kappou

Fixed Income and Equity Investments deals with the valuation of fixed income and equity securities. The module focuses on the basic characteristics of these securities and the strategies used for estimating their fundamental value and assessing their risk. Its primary aim is to discuss how certain characteristics and relationships can affect the value of fixed income and equity securities and how can they be exploited to form optimal investment strategies or for the purpose of conducting financial analysis. The analytical techniques introduced in this module are widely applied in other elements of the programme.

Teaching Staff

Dr Miriam Marra

This module introduces to students the mathematical tools of probability, calculus and stochastic calculus needed for the valuation of financial derivatives. The course covers the basic concepts and methods of selected areas of modern probability, calculus and stochastic analysis placing emphasis on the possible applications in finance.

Teaching Staff

The objective is to introduce the students to the C++ language and its usage for financial models implementation. The aim is to quickly reach a stage where students can produce a working and efficient code at the expense of a detailed presentation of the language. The focus is on using the more straightforward and most widely used features of C++. Special emphasis is placed on coding style and some essential software engineering principles are introduced. To achieve good productivity, the following topics are covered: development, building, debugging, testing, and optimising code in the Visual Studio environment. Other tools used are Excel interfacing and source control with Subversion (svn). Some facilities from the standard template library (STL) and Boost library are also used.

Teaching Staff

The module is designed to provide an introduction to the models and pricing of interest rates and credit derivatives. It conveys the basic concepts and analytical methodology for the valuation of derivatives in the standard Black-Scholes framework. It also builds awareness of the mathematical foundation for working in the area of financial derivatives’ pricing.

Teaching Staff

Dr Emese Lazar

This module introduces the major numerical methods required for quantitative work in finance, with a particular emphasis on the tools required for the implementation of the major derivative pricing models.

This module aims to introduce the models and pricing methodologies characteristic for three markets, namely equity and foreign exchange, interest rate and credit derivatives markets. For the equity and FX derivatives markets it aims to introduce models beyond Black-Scholes to price non-vanilla instruments. For interest rate derivatives markets arbitrage-free term structure models are considered. For credit derivatives we introduce the default intensity approach for the valuation of single name default swaps and the pricing of OTC derivatives in the presence of counterparty risk.

Teaching Staff

Building on the material introduced in Quantitative Methods for Finance, this module covers a number of more advanced techniques that are relevant for financial applications, and in particular for modelling and forecasting financial time series. These include an introduction to maximum likelihood estimation and two-stage least squares, models of volatility, simulation techniques, and multivariate models. Case studies from the academic finance literature are employed to demonstrate potential uses of each approach. Extensive use is also made of financial econometrics software to demonstrate how the techniques are applied in practice.

Teaching Staff

Professor Michael Clements

The purpose of the module is to provide an understanding of the latest developments in banking regulations that are the main driving force behind changes in our approaches to risk measurement. It focuses on the foundations of market risk analysis and the basic models for assessing market risk. Participants utilise market risk measurement techniques that are used daily in the front and middle offices of banks; particular emphasis is placed on the appraisal of the covariance matrices that are used to measure the market risk of portfolios. They also learn to build various Value-at-Risk (VaR) models for market risk for international portfolios of equities, FX, interest rate products, commodities, derivatives etc. The module has a significant practical component with computer-based workshops that are designed to support the lecture material.

The module objective is to give students a practical working knowledge of the pricing, hedging and trading of derivative securities, in particular options, via the use of trading simulations and pricing case studies/software. The emphasis of the module is on practical application and it is expected that by the end of the module students will understand and be able to analyse the time/risk dynamics of derivatives in a trading environment. Trading sessions are delivered in our state-of-the-art dealing rooms and utilising our bespoke trading simulation software ICTrader, offering exposure into real trading environment and the price formation process. Students will learn how to “think on their feet”, an essential skill in the investment banking industry.

Teaching Staff

Dr Michael Smith

The aim of the research project is to allow students to define and execute a piece of research in finance on a topic of their choice, with direction from an academic supervisor and with assistance from a doctoral student support supervisor.

Teaching Staff

Professor Charles Sutcliffe

Fixed Income Cash and Derivative Markets applies more advanced valuation and risk assessment methods that build on the knowledge introduced in the fixed income component of the first term Fixed Income and Equity Investments module: It describes the basic characteristics of fixed income derivatives, structured products and credit sensitive securities and develops practical strategies for valuation and risk assessment. It also considers how the markets for these securities are related and begins the task of showing how these relationships can be exploited for trading or investment. The module is designed not only for students wanting a more advanced knowledge of the fixed income markets, but also for students wishing to take the exam for the ICMA Fixed Income Certificate (ICMA FIC)

Teaching Staff

This module aims to provide students with a detailed knowledge of the commodity derivatives markets. It examines the aspects of pricing and trading physical derivatives, with emphasis on the energy and shipping (freight) sectors. The course is designed using real-life trading examples, stimulating students, who wish to follow a sales and trading career, to approach derivatives pricing from first principles.

Teaching Staff

Dr Konstantina Kappou

In this module you will learn how machine learning techniques borrowed from artificial intelligence can be used to solve common problems in finance. With the use of Python, we will explore ways in which a computer can be trained to recognise patterns in data. The focus will be on finance applications including stock price forecasting, default prediction and market sentiment analysis.

Teaching Staff

In this module you will learn how big data techniques can be used to solve problems in finance. We will first explore issues related to the collection, organisation and visualisation of large sets of structured and unstructured data. We will then look at methods for storage and computation of big data sets by distributed computing (Hadoop). The module will also explore the use of cloud computing platforms with a focus on the Google Cloud Platform (GCP).

Teaching Staff

This module aims to provide students with an understanding of financial decision making in the context of the energy industry. The course will combine theoretical models with practical applications. It focuses on energy markets (products, companies, production and consumption), capital budgeting in energy companies, financing of energy companies, energy derivatives and trading in energy markets. A number of case studies in energy finances are utilised.

Teaching Staff

Professor Marcel Prokopczuk

This module is designed for advanced Master’s students and doctoral students. It has a very high technical content. It aims to equip the students with the foundations of theoretical asset pricing and with the relevant skills for performing empirical tests. Additionally, a few important corporate finance topics will be covered in the format of student presentations. The objective of the module is to prepare students to become independent and quality researchers.

Teaching Staff

Professor Marcel Prokopczuk

The module is less quantitative option open to all MSc students that builds on the coverage of futures contracts from term 1. By the end of the module it is expected that students will be aware of the different ways of constructing stock market indices and the implications of these differences, how futures contracts are traded and the identity of some of the close substitutes for trading index futures, how futures can be priced using an arbitrage relationship, how futures can be used for hedging the price risk of the underlying, and the various uses that fund managers make of these instruments.

Teaching Staff

Professor Charles Sutcliffe

This module gives students the opportunity to pursue a work placement with an external organisation broadly related to the general sphere of their degree studies. The aim of the module is to allow participants to gain work experience in a career path of interest, develop a wide range of employability skills, build their network and enhance market awareness. The maximum duration of the placement is 3 months and it takes place during the summer vacation period (June-August). Placements should be secured by students independently. The Centre’s career development office can support students in their search and application process. Placements secured by students are subject to the approval of the module convenor. The module is assessed by a 3,000-word project based on the work experience gained.

Teaching Staff

Dr Michael Smith