Module manager: Dr Nadhir Ben Rached
Email: N.BenRached@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
Intermediate knowledge of programming in Python, e.g. MATH5306M Introduction to Programming or similar. Implementations of numerical algorithms will be required in assessed coursework. No training in programming will be provided.
| MATH5330M | Continuous Time Finance |
This module is not approved as an Elective
The aim of this module is to introduce the standard computational methods in financial mathematics and their application to securities pricing. There is a strong emphasis on practical implementation and numerical simulation.
On completion of this module, students will be able to:
- explain the basic modelling tools for financial options;
- generate random numbers with specified distributions;
- numerically solve stochastic differential equations;
- carry out Monte Carlo simulations to price financial derivatives;
- explain techniques for improving simulation accuracy and efficiency;
- demonstrate an understanding of numerical methods for PDEs;
- use finite difference methods to price European and American options;
- estimate price sensitivities (the Greeks).
Pricing financial instruments and valuing new securities requires advanced numerical methods. Financial analysts routinely apply computational techniques to assess risk, price exotic options or value interest rate derivatives. A good command of the essential computational tools used in the financial service industry is expected from quantitative analysts.
This module covers Monte Carlo simulations and finite difference methods for financial derivative pricing. These two methods (both are standard workhorses) are essential in analysing securities that are modelled by stochastic or partial differential equations. Practical skills are emphasised and students will learn how to implement (and improve accuracy and efficiency of) numerical methods for financial valuation.
On completion of this module the student will be familiar with numerical methods in financial mathematics and will be able to apply and implement these methods to price financial derivatives.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 11 | 2 | 22 |
| Practical | 3 | 1 | 3 |
| Seminar | 11 | 1 | 11 |
| Private study hours | 114 | ||
| Total Contact hours | 36 | ||
| Total hours (100hr per 10 credits) | 150 | ||
2- 5 hours per lecture: 55 hours
- 5 hours per class: 55 hours
- Preparation for assessment: 20 hours
Exercises will be handed in for assessment on a bi-weekly basis.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | . | 40 |
| Total percentage (Assessment Coursework) | 40 | |
The resit for this module will be 100% by examination
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 2.0 Hrs 0 Mins | 60 |
| Total percentage (Assessment Exams) | 60 | |
The resit for this module will be 100% by 2 hours examination
The reading list is available from the Library website
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team