Aims - This module aims to explore advanced topics in finance via mathematical and statistical methods in order to gain a better understanding of optimal decision making, risk management and derivative pricing techniques. It is primarily intended for third and fourth year undergraduate students and taught postgraduate students registered on the degree programmes offered by the Department of Statistical Science, or jointly with other departments. The academic prerequisite for such students is STAT0013.

Objectives - On successful completion of the module, a student should be able to: define the concepts of risk aversion and stochastic dominance, and apply them to manage risk in, and rank capital projects; understand how dynamic programming can be used to make optimal decisions under uncertainty; understand how to apply mathematical and statistical modelling techniques to credit risk modelling, value-at-risk measurements and capital adequacy assessments; understand a range of modelling techniques used in derivative pricing, and the concepts and assumptions that underpin them; criticise and understand the limitations of these techniques as they are used in the modern finance industry.

Applications - The techniques taught in this module are widely used throughout the modern finance industry, including the areas of: business investments decisions (for example in the energy sector where decisions on whether or not to invest in and build new power plants are subject to uncertainty regarding future energy demand and prices); in corporate finance; in trading activities in the financial markets; in financial and other forms of risk management; in valuing and accounting for assets; and in the prudential regulation of the banking industry.

Content - Utility theory; Real options, including dynamic programming, optimal investment rules, and managerial flexibility; Risk management, including value-at-risk and credit risk modelling; More advanced techniques in derivative pricing.

Key texts - Available from ReadingLists@UCL.