Who is it for?
Interested in tackling changes in the financial market in a mathematically sound manner? The MSc in Financial Mathematics will give you the skills to design, implement and change pricing models and analytical tools for risk management, or push new quantitative modelling ideas across different asset classes.
To successfully complete the Financial Mathematics postgraduate course, you must have a very good understanding of mathematics. You may well have studied maths, physics or engineering degrees as an undergraduate.
Or you might have a bachelor’s degree in economics or science and in particular computer science, which, coupled with your interest in stochastic modelling, could also qualify you for this programme.
You should have a general interest in learning the more technical and mathematical techniques used in financial markets; but you don’t need to have a background in finance.
Objectives
The master's in Financial Mathematics focuses on stochastic modelling and simulation techniques, but also covers econometrics, asset pricing, risk management, and offers an introduction to key financial securities such as equities, fixed income products and derivatives.
You will be taught Python and Matlab during terms 1 and 2, and you will have the opportunity to learn other programming languages as part of our electives offering, such as VBA or C.
Term three offers you flexibility within your masters; either by writing a dissertation or undertaking a project, or by completing your postgraduate degree entirely choosing electives.
Structure
What will you learn
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You will gain a very good understanding of the technical aspects used in financial markets, including wide ranging financial theory and different financial assets.
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You will gain a sound knowledge of stochastic modelling and mathematical finance, and also a good understanding of econometrics and programming, in particular Python and Matlab.
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From the MSc Financial Mathematics you will also understand how the theory is being applied in the financial industry and what practical issues are.
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In the third term you have three different options how you can complete your MSc, including a project or choosing only electives. Popular electives include Modelling and Data Analysis, Advanced Financial Engineering and Credit Derivatives, Credit Risk Management, Quantitative Risk Management. Introduction to Python.
Induction weeks
All of our MSc courses start with two compulsory induction weeks which include relevant refresher courses, an introduction to the careers services and the annual careers fair.
Term 1
Core modules:
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Asset Pricing
This module focuses on the introduction of pricing financial securities, which forms the basis for understanding asset pricing behaviour and the cornerstone of many asset pricing models. The focus is on spot securities, mainly equities and debt instruments.
The module also introduces students to the fundamental theory used by practitioners and academics in the wider field of finance, in particular asset management. That includes portfolio theory, the CAPM, factor models and measuring risk and return.
Those concepts are widely used by financial market participants. At the end of this module the various building blocks are being put together in the discussion of performance and persistence of performance of mutual funds.
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Derivatives
To introduce derivatives and derivative models in the context of financial risk management. To complement general finance courses with specific instruction in the key derivatives area.
To enable you to use models in this area in practical applications. To transmit to you the fundamental mathematical modelling techniques underpinning the subject.
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Foundations of Econometrics
The course provides the essential statistical and econometric techniques needed to conduct quantitative research in finance and economics.
This combination of econometric theory and application will enable you to understand and interpret empirical findings in a range of financial markets, including reading of empirical academic literature and critical assessment econometric applications undertaken by industry practitioners.
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Stochastic Modelling Methods in Finance
The module provides the necessary mathematical tools on which the entire programme is based.
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To introduce you to Brownian motion and stochastic calculus
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To provide examples of applications of stochastic calculus in financial areas
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To provide the tools required for a rigorous understanding of financial modelling and pricing techniques
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To learn fundamental numerical methods for simulating trajectories of commonly used stochastic processes.
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Applied Research Tools
Strong research skills are a key element of development strategy for companies and institutions large and small. In particular the ability to programme and to automate procedures. This module focuses on Python as a programming language and students will learn the basics in term 1 with some applications to finance being introduced in term 2.
The module introduces the main programming skills which are helpful in the financial industry. Operating on matrices, loops, conditional statements, subroutine/functions/procedures and optimisations are core skills which are being introduced in this module.
Term 2
Core modules:
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Fixed Income
To provide a foundation in a crucial area of financial markets and quantitative finance. To complement the general derivatives course with specific instruction in a key derivatives area.
To acquaint you with the main modelling streams in fixed income securities. To enable you to use models in this area in practical applications. To transmit to you the fundamental mathematical modelling techniques underpinning the subject.
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Risk Analysis
Financial disasters are a constant reminder of the relationship between financial risk and reward. The quantitative approach to this relationship is ever more dominant in the market and subject to constant innovation.
As market participants need to keep abreast of new developments, the Risk Analysis module provides a good path of study in this field.
The aim of this module is to help you develop a solid background for evaluating, managing and researching financial risk. To this end you will learn to analyse and quantify risk according to current best practice in the markets.
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Advanced Stochastic Modelling
The module introduces more recent developments in the field of financial mathematics to:
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Introduce you to more recent advances in mathematical finance
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Provide you with the mathematical tools required for the setting up of more sophisticated financial models and valuation framework
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Introduce you to pricing frameworks that go beyond the Black-Scholes model and the necessary numerical methods.
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Simulations Techniques and Financial Modelling
This module focuses on applications of numerical methods and programming languages to finance. Students will learn how to generate scenarios using popular financial models, like the Black-Scholes model, the Heston model, the Variance Gamma model and more. Students will also gain a sound knowledge of Fourier-based methods, simulation methods such as Monte Carlo and empirical bootstrap, in addition to covering a primer in Artificial Neural Networks. Focus is placed on applications in option pricing, risk management and portfolio performance evaluation.
Term 3
You may choose from the three options in your final term.
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Option 1: Students can take five specialist elective modules (5 x 10 credits).
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Option 2: Students can opt to write a 10,000-word Business Research Project (40 credits) and take one specialist elective module (1 x 10 credits).
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Option 3: Students can opt to write a 3,000-5,000-word Applied Research Project (20 credits) and take three specialist elective modules (3 x 10 credits)
Projects
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Business Research Project
It is important for aspiring professionals to demonstrate, on an individual basis, their ability to apply concepts and techniques they have learned in an in-depth study of a topic of their choice and to organise their findings in a report, all conducted within a given time limit.
To train you to undertake individual research and provide you with an opportunity to specialise in a contemporary business or finance topic related to your future career aspirations.
You are required to submit a project of approximately 10,000 words on any subject area covered in the rest of the programme.
Typical projects can involve any of the following: extracting data from electronic databases or by hand; statistical analysis of large or small populations; interviews; case studies of an industry or a sector or of a business / finance issue in a particular country setting.
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Applied Research Project
The aim of this module is to enable you to demonstrate how to integrate your learning in core and elective modules and then apply this to the formulation and completion of an applied research project.
You will be required to demonstrate the skills and knowledge that you have acquired throughout your MSc study.
You will undertake a short piece of applied research on a question of academic and/or practical relevance. Guidelines will be provided in order to help you identify the research question.
Based on your chosen topic, you must write a report of around 3,000–5,000 words that summarises and critically evaluates your method and your findings.
Electives offered in 2021
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Applied Machine Learning
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Advanced Financial Modelling and Forecasting
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Behavioural Finance
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Ethics, Society and the Finance Sector
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Financial Crime
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Hedge Funds
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Technical Analysis and Trading Systems
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Trading and Hedging in the Forex Market
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VBA with Application for Finance.
International electives
*Please note that electives are subject to change and availability.
