Designed in conjunction with leading risk professionals, this programme aims to meet growing demand for professionals who are highly skilled in quantitative risk management. Students gain core competencies in risk analysis and have the opportunity to tailor the programme to their own interests and needs through the diversity of options available. The course is practical and data driven; a substantial part consists of a summer project usually undertaken as a placement in a major bank in the City of London.
There is major interest in the Bank of England, the FSA and the Financial Services Industry to raise the level of quantitative analytics used in risk management and compliance - UCL, in collaboration with the B0E/FSA, aims set a new benchmark in this area, based on turning out risk professionals who are good scientists in the area of risk management.
MSc Financial Risk Management consists of 8 taught modules (4 core, 4 optional) and a dissertation.
Core Modules Term 1
COMPG004 Market Risk Measures and Portfolio Theory
The module aims to familiarise students with key concepts and models in general asset pricing, portfolio theory, and risk measurement. Those concepts and models include risk aversion, utility functions as a representation of preferences, efficient frontiers, Markowitz Portfolio theory, the Capital Asset Pricing model, Value at Risk, and Expected Shortfall.
COMPG008 Stochastic Processes for Finance
Rehearse/survey probability theory and give a systematic introduction to stochastic processes and their applications without stressing too much the measure-theoretical aspects and other mathematical formalisms. The module is aimed at students with an undergraduate degree in engineering, physics, computer science and the like, who have a good basis in calculus and have already come into contact with aspects of probability and statistics for ad hoc applications like transport equations, laboratory data treatment, and quantum mechanics, but have not attended yet a dedicated course on stochastic processes. The course material will unfold with references to its historical development and early applications in physics/engineering the students may already heard of, ending with current-day applications in finance.
COMPG012 Financial Engineering
An introduction to the applied mathematical and computational aspects of Quantitative Finance.
Core Modules Term 2
COMPG001 Financial Data and Statistics
The course is aimed at introducing to financial data analytics. The course is primarily focused on the observation of financial market dynamics of both individual assets and collective group of assets and the individuation of regularities, patterns and laws from a statistical perspective. Instruments to analyse, characterize, validate, parameterize and model complex financial datasets will be introduced. Practical issues on data analysis and statistics of high frequency and low frequency financial data will be covered.
Core Modules Dissertation
COMPGF98 MSc Financial Risk Management Project
Between June and August students do a research project resulting in a thesis of about 10,000 words or 50 pages. This is usually undertaken within a summer placement in an industry environment organised by one of the Programme Directors, Donald Lawrence, with both an academic and an industrial supervisor. This gives students experience of conducting project work in a real-life setting and may lead to the offer of a permanent job at the end of the project; so far this happened in 20-30% of the cases.
In recent years, commercial partners have included AlgoDynamix, Algo Trading, Almanis, AXA, Banking Science, BNP Paribas, Chapelle Consulting, Citibank, Commerzbank, Credit Suisse, Deutsche Bank, Ernst&Young, Fund Apps, Gain Capital, Intel, LCH.Clearnet, Liberis, Morgan Stanley, Mysis, Message Automation, Nomura, Oasis AWS, OptiRisk, Principal Financial Group, PricewaterhouseCooper, Royal Bank of Scotland, Santander, Société Générale, Thomson Reuters and TSB Bank. Every year there are changes to this list and, although all students have been placed in previous years, there is no guarantee for the future, so that it cannot be excluded that, especially in the case of an economic downturn, students may need to resort to a research project internal to UCL with only an academic supervisor.
Optional Modules Term 1
COMPG005 Numerical Analysis for Finance
The module aims to give students an introduction to numerical/computational methods and techniques with code examples in Matlab and an emphasis on applications in finance.
COMPG007 Operational Risk Measurement for Financial Institutions
The module aims to familiarise students with key concepts in the measurement and management of operational risk in the financial services. It will help them to understand the current issues and challenges faced by the sector, from a methodological, regulatory and financial standpoint. By detailing the most current debates in the field, the course aims at allowing the students to subsequently become positive agents of solutions in the market place and in research in operational risk.
COMPG013 Market Microstructure
This course provides the student with a structured overview over both the main empirical facts and major theoretical approaches in market microstructure. It will comprise of five main parts:
1) An introduction to limit order markets.
2) Empirical investigation of financial data.
3) Price impact.
4) The limit order book as a queuing system.
5) The relationship between impact, the bid-ask spread, the tick size, and liquidity.
COMPGS06 Financial Institutions and Markets
The module exposes participants to an overview of the financial information sector and interaction with global financial markets, which constitute an important application domain of computer science in the southeast UK as well as main global financial centers. The module facilitates transfer of substantial domain knowledge based on IB Analyst training program the lecturer delivers in major international firms.
MATHGF03 Equities, Foreign Exchange and Commodities
Further syllabus information can be found here.
Optional Modules Term 2
COMPG009 Networks and Systemic Risk
The first part of the course presents a general introduction to complex networks and dynamical processes. The second part is focused on specific applications to the study of contagion in financial networks. Overall, the course represents an introduction to the topic of systemic risk and stress propagation in networked systems.
COMPG014 Machine Learning with Applications in Finance
The module introduces students to the field of Machine Learning with a focus on supervised and unsupervised learning, presenting specific applications in Finance for each subtopic.
COMPGF03 Compliance, Risk and Regulation
The module will familiarize participants with compliance department processes in risk governance per requirements of regulators, shareholders, management and clients. Develop understanding of the major role of implementing the dynamic regulatory requirements in financial centers and the interdependence on risk IT, models and computational finance.
MATHGF06 Applied Computational Finance
Success in mathematical finance requires confidence and expertise in applying numerical analysis and programming to solve a wide range of pricing and risk management problems. This course presents numerical schemes for topics in derivative pricing together with programming in C++ and Python.
Further syllabus information can be found here.
STATG022 Quantitative Modelling of Operational Risk and Insurance Analytics
Further syllabus information can be found here.
Usually students choose their 4 optional modules (60 credits) from the programme diet above. If the timetables are compatible and upon authorisation by the Programme Director and the module lead, up to two optional modules may come from outside the programme diet. Modules taught in the UCL Departments of Computer Science (list), Mathematics (list), Physics (list) and Statistics (list) have good chances to be approved by the Programme Director.
An upper-second class UK bachelor's degree (or equivalent overseas qualification) in computer science, mathematics, statistics, physics, engineering or another similar quantitative subject. Graduates in economics, finance, business administration, actuarial science or similar are considered if their transcripts show a fair number of modules in mathematics, probability, statistics and econometrics with high marks. Programming experience is a plus, but not mandatory. Relevant work experience may also be taken into account.
English Language Requirements
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
The English language level for this programme is: Good
Further information can be found on our English language requirements page.
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
UK/EU fees (FT): £18,580 for 2017/18
Overseas fees (FT): £27,540 for 2017/18
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarship and Funding website.
Tuition Fee Deposit
This programme requires that applicants firmly accepting their offer pay a deposit. This allows UCL to effectively plan student numbers, as students are more demonstrably committed towards commencing their studies with us.
For full details about the UCL tuition fee deposit, please see the central UCL pages.
Tuition fee deposits within the Department of Computer Science are currently listed as:
|*where part-time is an available mode of study|
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Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Deadline 17 June 2017.