FIN40040 Numerical Methods

Academic Year 2023/2024

This module introduces many of the most important numerical methods used to solve scientific problems arising in financial applications. The module begins with an introduction to Python and the Python libraries NumPy and SciPy. The module then moves on to consider in some depth the solution of linear systems, numerical integration (including Monte Carlo methods), finite difference methods for partial differentiation, the solution of non-linear equations and numerical optimisation (including unconstrained and unconstrained optimisation). There are numerous Python examples used throughout each topic to illustrate the application of numerical methods in the area of quantitative finance.

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Curricular information is subject to change

Learning Outcomes:

Upon completion of this module students will be able to:

Solve large complex linear systems in Python using the most appropriate methods that are problem dependent

Intergrate functions deterministically using Quadrature methods and numerically using Monte Carlo methods

Interpolate and approximate functions using various methods such as splines and kernel regressions

Formulate and numerically solve discrete optimisation problems

Solve option pricing PDE problems numerically using finite difference methods

Carry out and interpret Monte Carlo studies, including derivatives pricing valuation.

Student Effort Hours: 
Student Effort Type Hours
Lectures

20
Specified Learning Activities

56
Autonomous Student Learning

84
Online Learning

12
Total

172
Approaches to Teaching and Learning:
My approach to teaching and learning is to deliver a mix of learning environments so that students attain coverage of the fundamentals but are also exposed to a variety of other learning environments to stimulate active learning. From my lectures that focus on theories and their application, to delivering small group tutorial classes that focus on problem solving and student presentations. My objective is to foster in students the ability to interpret, explain, apply, critically evaluate, and present the main methods of scientific computing in finance and the ability to use these methods in an informed manner in real world settings. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: Python based in class exam 2 hour End of Trimester Exam n/a Graded No

50
Continuous Assessment: Theory & Computing Assignments & Labs Varies over the Trimester n/a Graded No

50

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 12:00 - 13:50
Tutorial Offering 1 Week(s) - 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 14:30 - 15:20
External & School Exams Offering 1 Week(s) - 31 Thurs 15:30 - 17:00
Spring