MSc Financial Mathematics

Course Details

Six compulsory modules cover key material in finance, statistics and maths. Every year we offer many optional modules, available through various study routes: delivered here at WBS. Please note that availability and delivery modes may vary.

Modules are taught by staff from WBS, Warwick's Department of Statistics, and the Mathematics Institute through a combination of lectures, classes, and computer lab sessions. A one-week induction module will ensure you have the mathematical and statistical prerequisites for the course. Assessment is a mix of exams and coursework with your dissertation bringing all your learning together at the end.

Lectures & classes
Lectures introduce key theories, concepts, and economic models. You will solve financial problems and numerical exercises, analyse case studies, and make presentations of research published in academic journals.

Lab work
Lab work will give you hands-on experience of using software to perform finance-related calculations, conduct realistic simulations and write code. 

Your dissertation
A 10,000 word dissertation gives you the opportunity to test and apply techniques and theories you have been learning and to complete an original piece of research. You will be supervised and supported by one of our academic staff.




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Compulsory Modules

Asset Pricing

The aim of this module is to introduce students to the modern theory of asset pricing and portfolio theory in both static and dynamic settings.

C++ for Quantative Finance

This module will, in addition to teaching the foundations of object-oriented programming in C++, guide you through the development of solutions for a whole range of problems or methods covered elsewhere in the course.

Continuous Time Finance for Interest Rate Models

Further your knowledge of how stochastic calculus is used in continuous time finance and gain an in-depth understanding of models used for interest rates.

Financial Derivatives

This module provides an introduction to derivative securities and their pricing. The module aims to introduce various types of instruments traded in financial markets, along with the concepts of no-arbitrage pricing and hedging.

Numerical Methods

Gain a theoretical and practical understanding of numerical methods in finance, in particular those related to simulations of stochastic processes. In addition, you will receive an introduction to programming.

Probability & Stochastic Processes

Gain an introduction to the basic probability ideas which are of most relevance in finance and develop the machinery required to exploit these ideas.

Example Optional Modules

Behavioural Finance

Psychologists working in the area of behavioural decision-making have evidenced the inadequacies of neoclassical economics. In this module you will study financial markets using models that are less narrow than those based on von Neumann-Morgenstern expected utility theory and arbitrage assumptions.

Brownian Motion

Brownian motion is a fundamental tool for modelling processes which evolve randomly in time and underpins almost all stochastic models for asset prices in finance. In this module you will learn how to construct Brownian motion and study its path properties, how to use stochastic calculus for manipulations, and about differential equations. This will enable you to understand and develop state-of-the-art stochastic models in finance. 

Financial Risk Management
Gain an understanding of the need for financial risk management, the techniques to measure financial risks according to the regulatory framework, and the tools for the management of risk exposure. You will be introduced to quantitative methods of risk measurement and risk management.
Financial Time Series

Most financial data is available in time series form and therefore the statistics and modelling of time series data are essential components underpinning mathematical finance. The module aims to provide the relevant statistical theory and experience in financial time series statistics. Students will use statistical packages such as R to implement models.

Fixed Income & Credit Risk

Get to grips with the tools used for the assessment and management of fixed income and credit risk.

Bayesian Forecasting

This module is concerned with the theory and practice of short-term forecasting, using both data and subjective information. We focus on Dynamic Linear Models, a class of Bayesian forecasting models. Bayesian methods provide a natural framework for addressing central issues in finance. They allow investors to assess return predictability, estimation and model risk, formulate predictive densities for variances, covariance and betas. This can be done through decision theoretic problems, such as option pricing or optimal portfolio allocation.  

Partial Differential Equations in Finance

Gain a theoretical and practical understanding of partial differential equations, including numerical methods; link this understanding with problems from finance; gain an introduction to optimal control and Markov chain Monte Carlo (MCMC) methods.

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