COMP0041 Applied Computational Finance

This database contains the 2018-19 versions of syllabuses. These are still being finalised and changes may occur before the start of the session.

Syllabuses from the 2017-18 session are available here.

Academic session

2018-19

Module

Applied Computational Finance

Code

COMP0041

Module delivery

1819/A7P/T2/COMP0041 Postgraduate

Related deliveries

None

Prior deliveries

None - new delivery for 1819

Level

Postgraduate

FHEQ Level

L7

FHEQ credits

15

Term/s

Term 2

Module leader

Ahmad, Riaz

Contributors

Ahmad, Riaz

Module administrator

Nolan, Martin

Aims

Mathematical finance topics studied in term 1 are extended to consider areas of numerical methods used to solve pricing problems, where closed form solutions are not available. An introduction to programming in two popular languages of use in the financial markets.

Learning outcomes

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

  1. apply numerical schemes to solve pricing problems;
  2. demonstrate programming proficiency in basic C++ and Python to solve practical problems in Mathematical Finance.

Availability and prerequisites

This module delivery is available for selection on the below-listed programmes. The relevant programme structure will specify whether the module is core, optional, or elective.

In order to be eligible to select this module as optional or elective, where available, students must meet all prerequisite conditions to the satisfaction of the module leader. Places for students taking the module as optional or elective are limited and will be allocated according to the department’s module selection policy.

Programmes on which available:

  • MSc Computational Finance
  • MSc Financial Risk Management
  • MSc Financial Mathematics

Prerequisites:

In order to be eligible to select this module, students must have taken in Term 1:

Content

Success in mathematical finance requires confidence and expertise in applying numerical analysis and programming to solve a wide range of pricing and risk management problems. This course presents numerical schemes for topics in derivative pricing together with programming in C++and Python.

C++ continues to retain its ‘sexy’ status in the financial markets and is arguably the most popular language of use in Quantitative Finance. Python is rapidly becoming the standard in scientific computing, receiving much excitement about the application of Python to mathematical finance; its appeal continues to grow in both academia and industry. It is simple to use and free to download, with a growing amount of add-on modules. It is particularly easy to interface with C++.

Numerical Schemes: Binomial Model. Finite Difference Methods; Numerical Linear Algebra. Monte Carlo Methods.

C++: Data types; input/output; file management; control of flow and decision making. Functions; headers and source files. Arrays and strings. Pointers; dynamic memory allocation. Recursion. Objects and classes; operator overloading; polymorphism; inheritance.

Python: Introduction to some of the powerful libraries in Python - scientific manipulations (SciPy); data structures (NumPy); graphics (Matplotlib); data analysis (Pandas).

An indicative reading list is available via http://readinglists.ucl.ac.uk/departments/comps_eng.html.

Delivery

The module is delivered through a combination of lectures and programming sessions.

Assessment

This module delivery is assessed as below:

#

Title

Weight (%)

Notes

1

Coursework

40

2x closed book tests and 1 project

2

Written examination (2.5 Hours)

60

 

In order to pass this module delivery, students must achieve an overall weighted module mark of 50%.

Resources

Recommended Books

  • John Armstrong, C++ for Financial Mathematics, Chapman and Hall/CRC, 2017.
  • Rüdiger U. Seydel, Tools for Computational Finance, 5th Edition, Springer, 2011.