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FIN40040

Academic Year 2024/2025

Numerical Methods (FIN40040)

Subject:
Finance
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
8
Module Coordinator:
Dr Conall O'Sullivan
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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 Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): An end of trimester computer based exam covering the theory and practice of numerical methods in finance. Week 14 Standard conversion grade scale 40% No

50

 No
Exam (In-person): A midterm Python test covering the application of numerical methods to finance using Python. Week 7 Standard conversion grade scale 40% No

15

 No
Individual Project: Individual project implementing and interpreting the output from financial models that require the use of numerical methods. Week 8 Standard conversion grade scale 40% No

15

 No
Group Work Assignment: Group project where each member's contribution is described in the group report. Group members may be awarded different grades based on their individual contribution. Week 10 Standard conversion grade scale 40% No

20

 No

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.

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