MSc in Quantitative Methods for Risk Management

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MSc in Quantitative Methods for Risk Management

Page contents > Papers 4 & 5 options list | Prerequisite Requirements and Mutually Exclusive Options

Programme Code: TMQMRM

Department: Statistics

For students starting this programme of study in 2023/24

Guidelines for interpreting programme regulations

Classification scheme for the award of a taught master's degree (four units)
Exam sub-board local rules

Students take three compulsory half unit courses and 2.5 units of optional courses.

Students are required to take a two-week compulsory introductory course MA400 September Introductory Course (Financial Mathematics) in September.

Please note that places are limited on some optional courses. Admission onto any particular course is not guaranteed and may be subject to timetabling constraints and/or students meeting specific prerequisite requirements.


Paper	Course number, title (unit value)
Introductory Course	MA400 September Introductory Course (Financial Mathematics and Quantitative Methods for Risk Management) (0.0)
Paper 1	ST409 Stochastic Processes (0.5) ^#
Paper 2	ST429 Statistical Methods for Risk Management (0.5) ^#
Paper 3	MA417 Computational Methods in Finance (0.5) ^#
Papers 4 & 5	Courses to the value of 1.5 unit(s) from the following:
	MA411 Probability and Measure (0.5) ^#
	MA415 The Mathematics of the Black and Scholes Theory (0.5) ^#
	MA416 The Foundations of Interest Rate and Credit Risk Theory (0.5) ^#
	MA420 Topics in Financial Mathematics (0.5) ^#
	MA435 Machine Learning in Financial Mathematics (0.5) ^#
	ST422 Time Series (0.5) ^#
	ST426 Applied Stochastic Processes (0.5) (not available 2023/24)
	ST436 Financial Statistics (0.5) ^#
	ST439 Stochastics for Derivatives Modelling (0.5) ^# (not available 2023/24)
	ST440 Recent Developments in Finance and Insurance (0.5) ^# (not available 2023/24)
	ST443 Machine Learning and Data Mining (0.5) ^#
	ST446 Distributed Computing for Big Data (0.5) ^#
	ST448 Insurance Risk (0.5) ^# (not available 2023/24)
	ST449 Artificial Intelligence (0.5)
	ST451 Bayesian Machine Learning (0.5) ^#
	ST455 Reinforcement Learning (0.5) ^#
	ST456 Deep Learning (0.5) ^#
	ST457 Graph Data Analytics and Representation Learning (0.5) ^#
Paper 6	Courses to the value of 1.0 unit(s) from the following:
	FM441 Derivatives (0.5) ^#
	FM442 Quantitative Methods for Finance and Risk Analysis (0.5) ^#
	MA409 Continuous Time Optimisation (0.5) ^#
	ST452 Probability and Mathematical Statistics I (0.5)
	ST453 Probability and Mathematical Statistics II (0.5) ^#
	Further half-units(s) from the Paper 5 options list, or from other appropriate MSc courses subject to the approval of the Programme Director and the teacher responsible for the course.
	Papers 4 & 5 options list
Additional course 1	Students taking FM442 can apply for a place on the following non-assessed computer course:
	FM457 Applied Computational Finance (0.0) (withdrawn 2023/24)
Additional course 2	Students can also take the following non-assessed course taken in addition to the required five compulsory half unit courses and three half units of optional courses detailed above:
	MA422 Research Topics in Financial Mathematics (0.0)

Papers 4 & 5 options list

MA411 Probability and Measure (0.5) ^#
MA415 The Mathematics of the Black and Scholes Theory (0.5) ^#
MA416 The Foundations of Interest Rate and Credit Risk Theory (0.5) ^#
MA420 Topics in Financial Mathematics (0.5) ^#
MA435 Machine Learning in Financial Mathematics (0.5) ^#
ST422 Time Series (0.5) ^#
ST426 Applied Stochastic Processes (0.5) (not available 2023/24)
ST436 Financial Statistics (0.5) ^#
ST439 Stochastics for Derivatives Modelling (0.5) ^# (not available 2023/24)
ST440 Recent Developments in Finance and Insurance (0.5) ^# (not available 2023/24)
ST443 Machine Learning and Data Mining (0.5) ^#
ST446 Distributed Computing for Big Data (0.5) ^#
ST448 Insurance Risk (0.5) ^# (not available 2023/24)
ST449 Artificial Intelligence (0.5)
ST451 Bayesian Machine Learning (0.5) ^#
ST455 Reinforcement Learning (0.5) ^#
ST456 Deep Learning (0.5) ^#
ST457 Graph Data Analytics and Representation Learning (0.5) ^#

Prerequisite Requirements and Mutually Exclusive Options

# means there may be prerequisites for this course. Please view the course guide for more information.

The Bologna Process facilitates comparability and compatibility between higher education systems across the European Higher Education Area. Some of the School's taught master's programmes are nine or ten months in duration. If you wish to proceed from these programmes to higher study in EHEA countries other than the UK, you should be aware that their recognition for such purposes is not guaranteed, due to the way in which ECTS credits are calculated.

Note for prospective students:
For changes to graduate course and programme information for the next academic session, please see the graduate summary page for prospective students. Changes to course and programme information for future academic sessions can be found on the graduate summary page for future students.