Financial Modelling and Data Science

• Credit value: 30 credits at Level 7
• Convenor: Dr Simon Hubbert
• Assessment: coursework (20%) and a three-hour examination (80%)

In this module you will become familiar with quantitative techniques used in financial modelling and you will gain knowledge of scientific details behind applications in data science. You will be introduced to stochastic processes, how they apply to finance and how they are used to build financial models. You will be given a grounding in the theory of derivative pricing and the numerical implementation of pricing models. You will be introduced to various tools from data science, including neural networks, support vector machines and kernel methods.

You will be equipped with necessary knowledge of financial modelling and data science, ready to work as a practitioner in the financial industry.

By the end of this module, you will:

• understand fixed income products and use them to construct risk-free and forward curves
• be aware of interest rate risk and understand strategies used to mitigate it
• understand the basic concepts of stochastic calculus, in particular Brownian motion and stochastic integrals
• understand Ito calculus and its application to stochastic differential equations (SDEs)
• be able to appreciate the connections between probability theory and partial differential equations
• be able to solve SDEs using Monte Carlo Simulation
• understand and apply the binomial method for option pricing
• understand the finite difference method for option pricing and appreciate the importance of stability of numerical methods
• understand the mathematics of neural networks
• understand the mechanics of support vector machines as a means of classify data
• be able to use kernel methods to solve option pricing problems.