Master of Science
MSc Quantitative Finance
Due to high demand for this course, we operate a staged admissions process with multiple selection deadlines throughout the year, to maintain a fair and transparent approach.
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Fees and funding
Fees
Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:
-
MSc (full-time)
UK students (per annum): £22,600
International, including EU, students (per annum): £36,800
The fees quoted above are fully inclusive of tuition, administration and computational costs.
Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .
Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
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Graduates of The University of Manchester and Manchester Metropolitan University can receive a 10% discount on their master's degree tuition fees as part of our Manchester Alumni Loyalty Discount scheme.
Course unit details:
Stochastic Calculus for Finance
| Unit code | BMAN71541 |
|---|---|
| Credit rating | 15 |
| Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
| Teaching period(s) | Semester 1 |
| Available as a free choice unit? | No |
Overview
The course takes a general probability theory-view to introduce students to fundamental concepts in finance, such as (Black-Scholes) option pricing, risk-neutral pricing, etc. The course starts off with an overview of general probability theory. For example, the first sessions deal with infinite probability spaces, filtrations, expectations, conditioning (i.e., sigma-algebras), etc. Having discussed this material, students are ready to learn about Levy processes, in particular, Brownian motion. Topics in this part of the course include: random walks, quadratic variation, the martingale property, first passage time, etc. The course then turns to stochastic calculus, for example, the Ito integral. These sessions will also offer a rigorous proof of the Black-Scholes model. The final important topic is risk-neutral pricing.
The course will be delivered via ten lectures. There are three (optional) tutorials. In the first tutorial, we practice the Ito-Doeblin formula. In the second, we use risk-neutral valuation techniques to derive the Black-Scholes-Merton formula. In the final tutorials, we derive important properties of a multi-asset market. The final lecture will be spent revisiting the material, discussing what topics are particularly relevant for the exam. Half of the sixth lecture will be used to discuss the assignment.
Pre/co-requisites
Aims
In this course unit, students learn about the basic mathematical tools necessary to understand fundamental concepts in quantitative finance. These mathematical tools include: the Markov property, Brownian motion, first passage time, the Ito-Doeblin formula, etc. Using these tools, students learn how to price plain-vanilla European call and put options. The course also demonstrates that every asset can be valued using risk-neutral pricing techniques.
The course aims to introduce the above concepts from a rigorous perspective: Mathematical proofs will almost always be offered. Despite this aim, the course instructor will also spend a great amount of time on discussing the underlying intuition behind the mathematical tools, in many cases using examples from the realm of finance. The main objective of the course is to introduce a small number of challenging mathematical propositions in such a way that (a) students can understand them and (b) know why they are relevant for Finance.
Learning outcomes
On completion of this unit successful students will have achieved the following learning outcomes:
· Understand the basic mathematical tools necessary to derive fundamental results in Finance, such as, for example, the Black-Scholes (1973) model:
- Ito’s Lemma;
- Brownian motion;
- Change of Measure;
- Etc.
· Independently use the above mathematical tools to value non-standard financial assets, such as exotic (say look-back) options.
· Simulate values from stochastic processes introduced during the course, and use these to price options via Monte-Carlo techniques.
Assessment methods
Coursework Assignment (50%)
Written Examination (50%)
Feedback methods
Informal advice and discussion during a lecture or workshop.
Responses to student emails and questions from the course director.
Detailed written comments (generic and specific) on assessed assignment.
Generic feedback posted on Blackboard regarding overall performance.
Recommended reading
The course will be entirely based on the following textbook:
Shreve, Steven (2004): Stochastic Calculus for Finance II: Continuous-time Models. Springer Finance Series, Springer, New York (ISBN: 144192311X).
Note that there is a second revised edition from 2010. The second edition covers the same material as the 2004 edition, but some mistakes in the earlier version have been corrected.
Study hours
| Scheduled activity hours | |
|---|---|
| Assessment written exam | 2 |
| Lectures | 30 |
| Practical classes & workshops | 3 |
| Independent study hours | |
|---|---|
| Independent study | 115 |
Teaching staff
| Staff member | Role |
|---|---|
| Kevin Aretz | Unit coordinator |
Additional notes
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