Syllabus for Probability and Martingales

Learning outcomes

On completion of the course, the student should be able to:

• give an account of basic properties of integration on probability spaces,
• give an account of basic convergence concepts and results,
• formulate and apply central theorems in probability and martingale theory, and be able to give an account of their proofs,
• account for financial models in discrete time, and be able to calculate replicating strategies and fair prices of financial claims,
• use the theory, methods and techniques of the course to solve problems.

Content

Probability spaces, probability measures and random variables. Integration with respect to a probability measure. Different notions of convergence and convergence theorems (monotone, Fatou, dominated). Radon-Nikodym derivatives and conditional expectations. Fair games and martingales, submartingales and supermartingales. Doob decomposition theorem. Stopping times and the optional sampling theorem. The upcrossing inequality and the martingale convergence theorem. The Doob maximal inequality and martingale transforms. Discrete time models in finance. Self-financing portfolios and value processes. Arbitrage opportunities and equivalent martingale measures. Completeness of a market. Options and option pricing.

Instruction

Lectures and problem solving sessions.

Assessment

Written examination at the end of the course (6 credits), and assignments during the course (4 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The course cannot be credited along with the course Probability theory 1MS038.