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MSc PG Dip Quantitative Finance and Mathematics

  • DeadlineStudy Details:

    MSc/Diploma Full-time and Part-time

Masters Degree Description

This masters in Quantitive Finance and Mathematics course introduces innovative modern mathematical techniques and financial modelling. It focuses on probability and stochastics, which are often not included in standard mathematics degrees. It’s been designed to include the practical and pragmatic aspects of mathematical finance – offering you relevant and modern skills relating to structured finance in demand in the UK and internationally. The aim is to understand, both quantitatively and qualitatively, the risks and uncertainty involved. And, the wide range of optional courses offers you the chance to tailor your learning experience to suit your interests.

Entry Requirements

Entry is aimed at graduates with strong mathematical knowledge and skills. A good honours degree in mathematics, statistics or a related subject with a substantial mathematical content is required. Motivation and a willingness to work hard are also important pre-requisites.

We are looking to train those who not only have a strong background in mathematics, but who also possess the ability to communicate effectively, and have the desire to succeed in a dynamic and competitive industry.

The programme is not suitable for finance professionals without graduate-level mathematical training.

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Fees

https://www.hw.ac.uk/study/fees-funding.htm

Student Destinations

Students who graduate from the programme will have excellent employment prospects that are not restricted to any one narrow sector of financial services.

Module Details

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core Courses:

  • Modelling and Tools;
  • Derivative Markets and Pricing;
  • Research and Industry Topics. 

Optional Courses:

  • Stochastic Simulation;
  • Statistical Methods;
  • Modern Portfolio Theory;
  • Optimization;
  • Enterprise Risk Management;
  • Data mining and Machine Learning;
  • Financial Markets;
  • Software Engineering Foundations;
  • Bayesian Inference and Computational Methods;
  • Financial Engineering;
  • Numerical Analysis (PDEs);
  • Advanced Derivative Pricing;
  • Numerical Techniques for PDE’s with either Time Series or Financial Econometrics;
  • Advanced Software Engineering.

Progression to the MSc project phase is dependent on assessed performance. Typical project topics may include:

  • Applications of multilevel Monte-Carlo sampling in finance;
  • An investigation of new numerical methods for stochastic interest rate models;
  • Space time adaptivity for Fokker—Planck equations.

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