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MSc Quantitative Finance

The Quantitative Finance MSc is a one-year full-time Masters programme designed for highly quantitative students with no finance or economics background who wish to pursue a career in banking or finance.

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About the course

Large investment banks such as HSBC and Barclays need people who can work on investment strategies and who can mathematically deduce how risky they are. This course is designed to give quantitative students a foothold in this financial world with the advanced, specialist skills that employers are seeking.

If you have a high level of mathematical ability, a background in a subject such as maths, engineering, physics or another highly quantitative subject and wish to move into finance, this could be the course for you. Graduates of this course leave with a complete toolkit of skills, from big data mining techniques to Python programming. We also offer modules in Python Programming, Stochastic Calculus, Financial Econometrics and Market Risk Forecasting and Control.

For a list of modules you will study, please take a look at our course content section.


Key Facts

Course Content

Our one-year MSc Quantitative Finance will provide you with analytical, technical and programming skills relevant to the analysis of risk and return in financial institutions and large corporations. 

There is considerable variety in the teaching methods used on the MSc in Quantitative Finance. These include lectures and discussion sessions, groupwork, case studies, presentations and workshops on the use of statistical packages.

The style of teaching is often highly interactive, so you will be contributing actively to class discussions and engaging in groupwork with students from many different backgrounds. As some of the modules are shared with other faculties and Management School programmes such as Money, Banking and Finance, Management Science and Finance, you will also have an opportunity to work with Masters students from many different programmes.

In your first term from October to December, you will take five core modules as below.

  • Quantitative Finance in Practice

    The aim of the module is to develop students' communication/presentation skills (oral and written), and generic skills which are needed in applied research in quantitative

    finance when communicating/collaborating with colleagues from their own discipline and from other disciplines. It will provide students with an understanding of the structure of a scientific paper or a consultancy report, and equip them with an understanding of how a quantitative argument is presented in written form. It will give students a good knowledge of how to summarise finance/quantitative finance papers (newspaper articles or academic papers) both verbally and in written form. It will equip students with skills to analyse, interpret and conduct a small quantitative investigation.

  • Foundations of Financial Markets

    This module will initially introduce students to core concepts in finance like time value of money, net present value analysis and alternative investment rules to assess investment decisions taken by firms and then moves on to the introduction of basic concepts related to financial markets, including definitions of key assets and market types as well as an understanding of the economics of financial markets with a focus on their functions, participants and organisational forms.

    This module will then provide students with a good understanding of fundamental theories and techniques in finance that are of concern to all financial market participants, such as bond markets and term structure of interest rates, economics of derivatives markets, forward and future contracts, swap agreements. The module will place a particular emphasis on understanding how quantitative methods and techniques are used in financial markets.

  • Financial Stochastic Processes

    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 (but not too rigorous) introduction to stochastic processes in general and their use in modeling 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. Specifically the course will include

    • Introduction to using R for computing.
    • Introduction to probability building from the axioms:
    • Univariate random variables: standard distributions and their justifications, interrelations, and properties;
    • Multivariate random variables: marginals and copulas, their decomposition as a series of conditionals, dependence measures, and standard distributions;
    • Simulation of random variables and approximation of their properties by Monte Carlo;
    • Transformations of random variables;
    • Markov processes;
    • Poisson and Gaussian processes covering stationarity, conditional independence, and standard examples;
    • Simulation of a range of stochastic processes to approximate properties.

  • Statistical Methods for Financial and Economic Applications


    There is substantial amounts of data collected which relates to business, financial or economic applications. Examples include data on stock returns and survey data used for credit scoring.

    This course 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.

    Specifically, the course will cover

    • Introduction to Inference: statistical models; estimators; confidence intervals.
    • Exploratory data analysis;
    • Likelihood inference: definition; calculating mles; asymptotic results; calculating confidence intervals; likelihood ratio statistic.
    • Regression models: linear regression; mle/least squares; model choice; ridge regression and variants; logistic and Poisson regression.
    • Value at Risk: nonparametric methods; model based estimation.
    • Time series models for volatility: ARCH and GARCH models.

    Implementation of analyses using R will be covered throughout the course. Statistical methods will be illustrated on applications including stochastic volatility and risk management.

  • Spreadsheet Modelling for Quantitative Finance

    This module provides an introduction to spreadsheet modelling skills in Excel. The outline syllabus will include the topics of:

    • Spreadsheet layout and model design
    • Data analysis including descriptive statistics and charts
    • VBA
    • Simulation in Excel
    • Optimisation in Excel

During your second term from January to March, you will choose six modules from those below.

  • Derivatives Pricing

    This module provides extensive coverage of methods used for valuing derivative securities in the investment banking industry, and includes an introduction to stochastic calculus.

    Topics covered include:

    • Discrete-time vs. continuous-time finance
    • Arbitrage pricing
    • Continuous processes
    • Stochastic calculus and Itô’s lemma
    • Hedging issues
    • Investment in derivatives
    • Continuous dividends
    • Black and Scholes model
    • Interest rate derivatives
    • Exotic derivatives

    This module contributes to the following CFA syllabus areas:

    • Derivative Investments (CFA levels I and II)

  • Financial Econometrics

    This module explains how econometric methods can be used to learn about the future behaviour of the prices of financial assets by using the information in the history of asset prices and in the prices of derivative securities. It also gives you practical experience of analysing market prices.

    It will help you to understand the important features of time series of market prices, appreciate the relevance of efficient market theory to predicting prices, and make you familiar with appropriate methods for forecasting price volatility. You will also learn how to use option prices to make statements about the distributions of future asset prices, gain experience of applying computational methods in Excel to stock market and currency prices, and develop your knowledge of a broad range of econometric methods that are applied in finance research.

  • Advanced Investment Management

    This module focuses on how financial theories are applied to investment management decisions. It will also critically analyse various portfolio management approaches used by professional investors in order to understand the strengths and weaknesses of these approaches.

    Although not intended to track the Chartered Financial Analysts syllabus, the module should prove useful to those intending to take this qualification or looking to enter the investment management industry as a portfolio manager or security analyst.

    This module contributes to the following CFA syllabus areas:

    • Portfolio Management (CFA levels I, II and III)

    Topics include:

    • Modern portfolio theory and its applications
    • Capital asset pricing model
    • Arbitrage pricing theory and its applications
    • Style investing: momentum, value, and size investing
    • Portfolio performance evaluation
    • Portfolio management approaches, including practical aspects of security selection, risk management and portfolio construction
    • Mutual funds, exchange-traded funds (ETFs) and hedge funds

  • Market Risk Forecasting and Control

    The aim of the module is to provide students with the hands-on time-series skills to competently estimate, test and interpret market-risk forecasting and control models & techniques which are required in the current regulatory environment: Value-at-Risk, Expected Shortfall, backtesting, extreme-value distributions, and copula models.

  • Behavioural Finance

    This module extends the analytical tools used for evaluating strategic and investment decisions learnt in other modules by deviating from the paradigm of rational decision making. It focuses on the implications of investor behaviour and capital market imperfections (such as limits to arbitrage) for investment management. The concepts you will cover on this module provide a foundation for value investing, arbitrage, asset management and opportunistic corporate finance. Insights from psychology and behavioural finance are used to complement traditional market frictions and explain the behaviour of capital markets.

  • Stochastic Calculus for Finance

    Stochastic Calculus is a theory that enables the calculation of integrals with respect to stochastic processes. It has wide-ranging applications, which have been particularly fruitful in mathematical finance. This module begins with the study of continuous-time stochastic processes, focusing on Brownian motion. Along the way, key concepts such as martingales and stopping times are encountered. The module then explores how to construct an integral with respect to Brownian motion. This leads on to the derivation of Ito's formula, a stochastic analogue of the chain rule, which is then used in the definition and solution of stochastic differential equations (SDEs), the stochastic analogue to ordinary differential equations (ODEs). The theory is then used to rigorously derive the Black-Scholes Formula for pricing financial options.

  • Assessing financial risk: Extreme Value Methods

    Assessment of financial risk requires accurate estimates of the probability of rare events. For example, in the next day of trading what is the risk of a share portfolio losing half of its value, or equivalently what is the value of the portfolio at risk of being lost with a specified probability...? 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 range of scenarios.

  • Introduction to Python Programming

    Python is a simple, yet very powerful, high level computer programming language that becomes immensely popular in our days. It is widely used in many scientific areas for data exploration and at the same time it is the preferred programming language among a wide range of modern organisations.

    This course is an introduction to computer programming using Python for students without any prior programming experience. It introduces the basic principles of computer programming but is doing it with an emphasis on examples from the areas of business analytics and finance.

  • Forecasting

    Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore Forecasting plays an important role in enhancing managerial decision making.

    After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.

  • Introduction to Intelligent Data Analysis (Data Mining)

    This module develops modelling skills on synthetic and empirical data by showing simple statistical methods and introducing novel methods from artificial intelligence and machine learning.

    The module will cover a wide range of data mining methods, including simple algorithms such as decision trees all the way to state of the art algorithms of artificial neural networks, support vector regression, k-nearest neighbour methods etc. We will consider both Data Mining methods for descriptive modelling, exploration & data reduction that aim to simplify and add insights to large, complex data sets, and Data Mining methods for predictive modelling that aim to classify and cluster individuals into distinct, disjoint segments with different patterns of behaviour.

    The module will also include a series of workshops in which you will learn how to use the SAS Enterprise Miner software for data mining (a software skill much sought after in the job market) and how to use it on real datasets in a real world scenario.

  • Optimisation

    The course is designed to enable students to apply optimisation techniques to business problems. Students are introduced to different mathematical optimisation models and solution algorithms, and shown how to use them to guide decision making in business and other organisations

Your final module focuses on your dissertation, with guidance from your academic supervisor. You can choose from a wide range of topics, and you will submit your dissertation at the beginning of September.

  • Dissertation

    From May to July you will be working on your Masters dissertation with guidance from your academic supervisor.

    The dissertation will be submitted at the beginning of September, at the end of the Masters Programme.

    There is a wide choice of dissertation topics provided by all 4 departments.

    The dissertation gives you the opportunity to apply all the theories and concepts you have seen during the year to a relevant research topic.


Our programme-specific scholarship for 2020 entry include the Academic Excellence, UK-EU and International scholarships aimed at high-achieving students, and the Nom Habu Scholarship in honour of a former staff member and student. We'll automatically consider you for these scholarships when you apply and if you are shortlisted we'll be in touch with the next steps, so it's best to apply as soon as possible. 

Nom Habu Scholarship

Lancaster University has announced the annual Nom Habu Scholarship in honour of its former staff and post graduate student, Nom Habu, who passed away after a short illness in December 2016. Nom was employed as the Regional officer, West Africa from May 2011 to August 2013, after which he came to study on the MSc Quantitative Finance.  On successful completion of his studies, he took post as the Student Recruitment Manager for Lancaster University Ghana.  The scholarship is lasting way of remembering Nom and his service during his time at the university.

The Nom Habu scholarship of up to £10,000 will be awarded to a Nigerian or Ghanaian student who has an offer to study MSc Quantitative Finance at Lancaster University for entry in October 2020. Any applicant meeting this criteria will automatically be invited to apply for the scholarship, so we recommend applying as soon as possible to be considered. For any questions regarding the Nom Habu Scholarship, please contact

We also offer LUMS Alumni scholarships - visit our Apply for Masters page to find out more.



The Careers Team at LUMS helps you shape your career plans and supports your job-hunting process in a variety of ways, including personalised one-to-one support and interactive workshops on areas such as career strategies, writing CVs and applications, interview skills, psychometric testing, what to expect at assessment centres, and online networking strategies.


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