This course provides an introduction to the theory of stochastic processes and its application in the mathematical finance area.

The course starts with background material on markets, modelling assumptions, types of securities and traders, arbitrage and risk minimisation. Basic tools needed from measure and probability, conditional expectations, independent random variables and modes of convergence are explained. Discrete and continuous time stochastic processes including Markov, Gaussian and diffusion processes are introduced. Some key material on stochastic integration, the theory of martingales, the Ito formula, martingale representations and measure transformations are described. The well-known Black-Scholes option pricing formula based on geometric Brownian motion is derived. Pricing and hedging for standard vanilla options is presented. Hedge simulations are used to illustrate the basic principles of no-arbitrage pricing and risk-neutral valuation. Pricing for some other exotic options such as barrier options are discussed. The course goes on to explore the links between financial mathematics and quantitative finance. Results which show that the transition densities for diffusion processes satisfy certain partial differential equations are presented. The course concludes with treatment of some other quantitative methods including analytic approximations, Monte Carlo techniques, and tree or lattice methods.

Mathematics of Finance provides an accessible but mathematically rigorous introduction to financial mathematics and quantitative finance. The course provides a sound foundation for progress to honours and post-graduate courses in these or related areas.

Note: This is an Honours Pathway Course. It continues the development of sophisticated mathematical techniques and their application begun in MATH3029 or MATH3320.

On successful completion of this course, students will be able to:

1. Explain the core mathematical tools and fundamental concepts of modern financial mathematics;
2. Solve a range of option pricing and hedging problems;
3. Apply the concepts of no arbitrage and risk minimisation in a range of quantitative finance contexts;
4. Demonstrate capabilities for advanced mathematical reasoning, analysis and modelling linked to the theory of stochastic processes.

Assessment will be based on:

• Assignments (50%; LO 1-4)
• Final examination (50%; LO 1-4)

MATH3029 OR MATH3320.