Overview:
- Credits:
- 7.5
- Level:
- 4
- Semester:
- Spring
- Subject:
- Finance
- School:
- Business
- Coordinator:
- Dr Conall O'Sullivan
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Curricular information is subject to change
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 Type | Hours |
---|---|
Lectures | 20 |
Specified Learning Activities | 56 |
Autonomous Student Learning | 84 |
Online Learning | 12 |
Total | 172 |
Not applicable to this module.
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.