Aims:

This module will introduce students to the field of modelling and analysing financial markets with emphasis on (i) the wide variety of deterministic and discrete-time methods that are available; and (ii) numerical simulation of the financial markets, including agent-based modelling. The module will start with a broad introduction to financial markets and terminology used in the financial markets.Learning outcomes:

On successful completion of the module, a student will be able to:

Distinguish between different types of modelling and analysis, and explain the advantages and disadvantages of each method.
	Understand discrete-time dynamic optimisation methods.
	Understand numerical simulation methods, including both agent-based techniques and the use of recurrence relations.
Content:

Introduction to the Financial Markets:

Market Microstructure.
	Order-driven and Quote-driven markets.
	Orders, Quotes and Trades.
	Post-trade processing.
	Regulation.
	Trading Strategies.
	Risk Management.
Markets:

Auctions.
	Markets.
	Dealer Markets and Order-Book Markets.
	Market Making.
	Low latency and High Frequency Trading.
Introduction to Techniques:

Game Theory.
	Minority Games.
	Agent Based Models.
	Dynamic Optimisation.
Specific models:

Day and Juang.
	Bulls, Bears and Market Sheep.
	Lyons.
	The Foreign Exchange Hot Potato.
	Huang et al.
	Optimal Market Making with Risk Aversion.
Requisites:

To be eligible to select this module as an optional or elective, a student must: (1) be registered on a programme and year of study for which it is a formally available; (2) have a UK-equivalent honours degree (or higher) in the field of Computer Science, Mathematics, Statistics, Physics, Engineering, or another similar quantitative subject; (3) have a strong background with high performance in mathematics; and (4) be proficient in the English language to UCL's "Good" standard or better.

Fundamental mathematics knowledge is required. Specifically, the student must be confident with fundamentals on study of functions, limits, differential and integral calculus, and the concept of probability. Some coding knowledge is also necessary. For MSc Computational Finance and MSc Financial Risk Management students, skills can be acquired in the first few weeks of term 1 with provided introductory courses and training sessions such as ‘Introduction to Mathematics and Programming for Finance’.