COMPGF04 - Financial Market Modelling and Analysis

This database contains the 2017-18 versions of syllabuses.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).


This module is suitable for MSc Computational Finance students. Students are expected to have the equivalent of a 2:1 UK bachelor's degree in computer science, mathematics, statistics, physics, engineering or another similar quantitative subject.

Programming experience is not necessary, but a strong background with high performance in mathematics is required.

English language at UCL's Good level is required.

Taught ByChris Clack (100%)

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 modeling.

Learning Outcomes

The students will be able to distinguish between different types of modeling and analysis, and explain the advantages and disadvantages of each method; they will gain an understanding of discrete-time dynamic optimisation methods; they will gain an understanding of numerical simulation methods, including both agent-based techniques and the use of recurrence relations.


  1. 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

  2. Markets

    • Auctions
    • Markets
    • Dealer Markets and Order-Book Markets
    • Market Making
    • Low latency and High Frequency Trading

  3. Introduction to Techniques

    • Game Theory
    • Minority Games
    • Agent Based Models
    • Dynamic Optimisation

  4. Specific models

    • Day & Juang - Bulls, Bears and Market Sheep
    • Lyons - The Foreign Exchange Hot Potato
    • Huang et al - Optimal Market Making with Risk Aversion
    • Instability arising from coupled dealer algorithms

Method of Instruction

Lecture presentations.


The course has the following assessment component:

  • Written Examination (2.5 hours, 100%)

To pass this course, students must:

  • Obtain an overall pass mark of 50%


Reading list available via the UCL Library catalogue.