• University of Edinburgh Featured Masters Courses
  • Leeds Beckett University Featured Masters Courses
  • Swansea University Featured Masters Courses
  • University of York Featured Masters Courses
  • Imperial College London Featured Masters Courses
  • Regent’s University London Featured Masters Courses
  • Xi’an Jiaotong-Liverpool University Featured Masters Courses
Birmingham City University Featured Masters Courses
Coventry University Featured Masters Courses
University of St Andrews Featured Masters Courses
University of Hertfordshire Featured Masters Courses
Newcastle University Featured Masters Courses
"asset" AND "price"×
0 miles

Masters Degrees (Asset Price)

  • "asset" AND "price" ×
  • clear all
Showing 1 to 11 of 11
Order by 
Programme structure. The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. Read more
Programme structure
The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. There are examinations and coursework in eight modules altogether, including the four core modules. Additionally, all students complete a dissertation.

Core modules
0.Probability and stochastics. This course provides the basics of the probabilistic ideas and mathematical language needed to fully appreciate the modern mathematical theory of finance and its applications. Topics include: measurable spaces, sigma-algebras, filtrations, probability spaces, martingales, continuous-time stochastic processes, Poisson processes, Brownian motion, stochastic integration, Ito calculus, log-normal processes, stochastic differential equations, the Ornstein-Uhlenbeck process.


0.Financial markets. This course is designed to cover basic ideas about financial markets, including market terminology and conventions. Topics include: theory of interest, present value, future value, fixed-income securities, term structure of interest rates, elements of probability theory, mean-variance portfolio theory, the Markowitz model, capital asset pricing model (CAPM), portfolio performance, risk and utility, portfolio choice theorem, risk-neutral pricing, derivatives pricing theory, Cox-Ross-Rubinstein formula for option pricing.


0.Option pricing theory. The key ideas leading to the valuation of options and other important derivatives will be introduced. Topics include: risk-free asset, risky assets, single-period binomial model, option pricing on binomial trees, dynamical equations for price processes in continuous time, Radon-Nikodym process, equivalent martingale measures, Girsanov's theorem, change of measure, martingale representation theorem, self-financing strategy, market completeness, hedge portfolios, replication strategy, option pricing, Black-Scholes formula.


0.Financial computing I. The idea of this course is to enable students to learn how the theory of pricing and hedging can be implemented numerically. Topics include: (i) The Unix/Linux environment, C/C++ programming: types, decisions, loops, functions, arrays, pointers, strings, files, dynamic memory, preprocessor; (ii) data structures: lists and trees; (iii) introduction to parallel (multi-core, shared memory) computing: open MP constructs; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.


0.Interest rate theory. An in-depth analysis of interest-rate modelling and derivative pricing will be presented. Topics include: interest rate markets, discount bonds, the short rate, forward rates, swap rates, yields, the Vasicek model, the Hull-White model, the Heath-Jarrow-Merton formalism, the market model, bond option pricing in the Vasicek model, the positive interest framework, option and swaption pricing in the Flesaker-Hughston model.

Elective modules

0.Portfolio theory. The general theory of financial portfolio based on utility theory will be introduced in this module. Topics include: utility functions, risk aversion, the St Petersburg paradox, convex dual functions, dynamic asset pricing, expectation, forecast and valuation, portfolio optimisation under budget constraints, wealth consumption, growth versus income.


0.Information in finance with application to credit risk management. An innovative and intuitive approach to asset pricing, based on the modelling of the flow of information in financial markets, will be introduced in this module. Topics include: information-based asset pricing – a new paradigm for financial risk management; modelling frameworks for cash flows and market information; applications to credit risk modelling, defaultable discount bond dynamics, the pricing and hedging of credit-risky derivatives such as credit default swaps (CDS), asset dependencies and correlation modelling, and the origin of stochastic volatility.

0.Mathematical theory of dynamic asset pricing. Financial modelling and risk management involve not only the valuation and hedging of various assets and their positions, but also the problem of asset allocation. The traditional approach of risk-neutral valuation treats the problem of valuation and hedging, but is limited when it comes to understanding asset returns and the behaviour of asset prices in the real-world 'physical' probability measure. The pricing kernel approach, however, treats these different aspects of financial modelling in a unified and coherent manner. This module introduces in detail the techniques of pricing kernel methodologies, and its applications to interest-rete modelling, foreign exchange market, and inflation-linked products. Another application concerns the modelling of financial markets where prices admit jumps. In this case, the relation between risk, risk aversion, and return is obscured in traditional approaches, but is made clear in the pricing kernel method. The module also covers the introduction to the theory of Lévy processes for jumps and its applications to dynamic asset pricing in the modern setting.

0.Financial computing II: High performance computing. In this parallel-computing module students will learn how to harness the power of a multi-core computer and Open MP to speed up a task by running it in parallel. Topics include: shared and distributed memory concepts; Message Passing and introduction to MPI constructs; communications models, applications and pitfalls; open MP within MPI; introduction to Graphics Processors; GPU computing and the CUDA programming model; CUDA within MPI; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.


0.Risk measures, preference and portfolio choice. The idea of this module is to enable students to learn a variety of statistical techniques that will be useful in various practical applications in investment banks and hedge funds. Topics include: probability and statistical models, models for return distributions, financial time series, stationary processes, estimation of AR processes, portfolio regression, least square estimation, value-at-risk, coherent risk measures, GARCH models, non-parametric regression and splines.

Research project

Towards the end of the Spring Term, students will choose a topic to work on, which will lead to the preparation of an MSc dissertation. This can be thought of as a mini research project. The project supervisor will usually be a member of the financial mathematics group. In some cases the project may be overseen by an external supervisor based at a financial institution or another academic institution.

Read less
Programme structure. The programme offers five "core" modules, taken by all candidates, along with a variety of elective modules from which students can pick and choose. Read more
Programme structure

The programme offers five "core" modules, taken by all candidates, along with a variety of elective modules from which students can pick and choose. There are lectures, examinations and coursework in eight modules altogether, including the five core modules. Additionally, all students complete an individual research project on a selected topic in financial mathematics, leading to the submission of a dissertation.

Core modules

Probability and stochastics. This course provides the basics of the probabilistic ideas and mathematical language needed to fully appreciate the modern mathematical theory of finance and its applications. Topics include: measurable spaces, sigma-algebras, filtrations, probability spaces, martingales, continuous-time stochastic processes, Poisson processes, Brownian motion, stochastic integration, Ito calculus, log-normal processes, stochastic differential equations, the Ornstein-Uhlenbeck process.

Financial markets. This course is designed to cover basic ideas about financial markets, including market terminology and conventions. Topics include: theory of interest, present value, future value, fixed-income securities, term structure of interest rates, elements of probability theory, mean-variance portfolio theory, the Markowitz model, capital asset pricing model (CAPM), portfolio performance, risk and utility, portfolio choice theorem, risk-neutral pricing, derivatives pricing theory, Cox-Ross-Rubinstein formula for option pricing.

Option pricing theory. The key ideas leading to the valuation of options and other important derivatives will be introduced. Topics include: risk-free asset, risky assets, single-period binomial model, option pricing on binomial trees, dynamical equations for price processes in continuous time, Radon-Nikodym process, equivalent martingale measures, Girsanov's theorem, change of measure, martingale representation theorem, self-financing strategy, market completeness, hedge portfolios, replication strategy, option pricing, Black-Scholes formula.


Interest rate theory. An in-depth analysis of interest-rate modelling and derivative pricing will be presented. Topics include: interest rate markets, discount bonds, the short rate, forward rates, swap rates, yields, the Vasicek model, the Hull-White model, the Heath-Jarrow-Merton formalism, the market model, bond option pricing in the Vasicek model, the positive interest framework, option and swaption pricing in the Flesaker-Hughston model.

Financial computing I. The idea of this course is to enable students to learn how the theory of pricing and hedging can be implemented numerically. Topics include: (i) The Unix/Linux environment, C/C++ programming: types, decisions, loops, functions, arrays, pointers, strings, files, dynamic memory, preprocessor; (ii) data structures: lists and trees; (iii) introduction to parallel (multi-core, shared memory) computing: open MP constructs; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.

Elective modules

Portfolio theory. The general theory of financial portfolio based on utility theory will be introduced in this module. Topics include: utility functions, risk aversion, the St Petersburg paradox, convex dual functions, dynamic asset pricing, expectation, forecast and valuation, portfolio optimisation under budget constraints, wealth consumption, growth versus income.

Information in finance with application to credit risk management. An innovative and intuitive approach to asset pricing, based on the modelling of the flow of information in financial markets, will be introduced in this module. Topics include: information-based asset pricing – a new paradigm for financial risk management; modelling frameworks for cash flows and market information; applications to credit risk modelling, defaultable discount bond dynamics, the pricing and hedging of credit-risky derivatives such as credit default swaps (CDS), asset dependencies and correlation modelling, and the origin of stochastic volatility.


Mathematical theory of dynamic asset pricing. Financial modelling and risk management involve not only the valuation and hedging of various assets and their positions, but also the problem of asset allocation. The traditional approach of risk-neutral valuation treats the problem of valuation and hedging, but is limited when it comes to understanding asset returns and the behaviour of asset prices in the real-world 'physical' probability measure. The pricing kernel approach, however, treats these different aspects of financial modelling in a unified and coherent manner. This module introduces in detail the techniques of pricing kernel methodologies, and its applications to interest-rete modelling, foreign exchange market, and inflation-linked products. Another application concerns the modelling of financial markets where prices admit jumps. In this case, the relation between risk, risk aversion, and return is obscured in traditional approaches, but is made clear in the pricing kernel method. The module also covers the introduction to the theory of Lévy processes for jumps and its applications to dynamic asset pricing in the modern setting.


Financial computing II: High performance computing. In this parallel-computing module students will learn how to harness the power of a multi-core computer and Open MP to speed up a task by running it in parallel. Topics include: shared and distributed memory concepts; Message Passing and introduction to MPI constructs; communications models, applications and pitfalls; open MP within MPI; introduction to Graphics Processors; GPU computing and the CUDA programming model; CUDA within MPI; applications to matrix arithmetic, finite difference methods, Monte Carlo option pricing.

Risk measures, preference and portfolio choice. The idea of this module is to enable students to learn a variety of statistical techniques that will be useful in various practical applications in investment banks and hedge funds. Topics include: probability and statistical models, models for return distributions, financial time series, stationary processes, estimation of AR processes, portfolio regression, least square estimation, value-at-risk, coherent risk measures, GARCH models, non-parametric regression and splines.

Research project

Towards the end of the Spring Term, students will choose a topic for an individual research project, which will lead to the preparation and submission of an MSc dissertation. The project supervisor will usually be a member of the Brunel financial mathematics group. In some cases the project may be overseen by an external supervisor based at a financial institution or another academic institution.

Read less
This specialist course gives you a thorough understanding of the full range of quantitative methods needed for financial decision making. Read more
This specialist course gives you a thorough understanding of the full range of quantitative methods needed for financial decision making.
– Learn how to forecast and manage risk and return
– Gain the skills to price any financial instrument
– Learn how to engineer new methods and financial products
– Build advanced knowledge of the main theoretical and applied concepts in quantitative finance, financial engineering and risk management, using current issues to stimulate your thinking
– Prepare for careers involving the design and management of new financial instruments, the development of innovative methods for measuring, or predicting and managing risk.

Recent recruiters
Bangkok Bank, Barclays Capital, Bloomberg, CIBC World Markets, Citigroup, Hewitt Associates, KPMG, MFC Fund, Schlumberger, Schneider, Tata Consultancy Services.

Course structure (All taught course units are 15 credits)

Semester 1
– Asset Pricing Theory
– Derivative Securities
– Stochastic Calculus for Finance

One elective unit from:
– Cross-Sectional Econometrics
– Portfolio Investment
– Scientific Computing

Semester 2
– Credit Risk Management
– Interest Rate Derivatives
– Time Series Econometrics

One elective unit from:
– Computational Finance
– Corporate Finance
– Credit Risk Management
– Generalised Linear Models and Survival Analysis
– Real Options in Corporate Finance
– Risk, Performance and Decision Analysis
– Simulation and Risk Analysis

Summer period
Dissertation (60 credits)
– Apply what you have learned in the taught part of the course
– Dissertations are supervised by both an academic expert and an industry practitioner
– Topics are aligned with the research interests of leading financial institutions from the City of London and internationally
– Industry-linked topics are subject to strict selection criteria (for example, quality of research proposal, strong CV and first semester exam performance).

Examples of recent dissertation project topics:
– Approximation of CVA/DVA/FVA
– FVA and MM – quantitative analysis/illustration
– Continuous rainbow options on commodity outputs
– Investigating dynamics and determinants of risk-neutral PDs
– Using hazard models to forecast corporate bankruptcy
– Analysing asset pricing implications from real options models
– Pricing sovereign CDS contracts
– Estimating liquidation probabilities of hedge funds

Open days

Masters information sessions

We are hosting a series of informal information sessions for undergraduates who are thinking about pursuing a Master’s course at Alliance Manchester Business School.

Our Masters courses aren't just for business graduates - from business analytics to operations, and marketing to finance, we have 17 courses to choose from. Join us to meet a careers advisor, admissions staff and current students and discover how our courses can boost your career prospects.

Also, a number of graduates have the opportunity to progress directly onto the Full-time MBA programme as a Young Potential Leader - could you be one of them?

Choose from the following dates:
Wednesday 15 February 2017, 12.00 - 1.30pm
Wednesday 15 March 2017, 12.00 - 1.30pm
Wednesday 26 April 2017, 12.00 - 1.30pm
Wednesday 10 May 2017, 12.00 - 1.30pm

All events are held in the Atrium, Alliance MBS East building (on the corner of Oxford Road and Booth Street East) - number 26 on the campus map.

For further information and to register your interest in attending, please see the Alliance MBS website: http://www.mbs.ac.uk/masters/meet-us.aspx

Read less
This programme is a comprehensive and intensive investigation into key areas in accounting and finance. Designed for those with a quantitative background, it is both academically rigorous and closely in line with professional practice. Read more

Programme description

This programme is a comprehensive and intensive investigation into key areas in accounting and finance. Designed for those with a quantitative background, it is both academically rigorous and closely in line with professional practice.

The MSc in Accounting and Finance is especially useful for those graduates with work experience in accounting looking to gain essential practical skills in finance - and, of course, vice versa. Although the compulsory core courses ensure a good balance between both accounting and finance study, the option courses give students the opportunity to specialise - tailoring their studies towards their chosen career.

Studying accounting and finance in Edinburgh gives students the opportunity to base themselves at the heart of the UK's second largest financial centre.

Many of Europe's leading financial institutions have their headquarters here, a fact that we make full use of on the MSc programme. We regularly bring guest speakers to the School to talk directly to accounting and finance students on real, current practice. The School also maintains good relationships with a number of accounting and finance professionals who will be on hand to provide advice on research and career opportunities. It is essential connections like these that characterise the dynamic nature of this strongly vocational programme.

Our strong connection to industry is exemplified by our work in the Centre for Financial Markets Research, and the Institute of Public Sector Accounting Research. Bringing together leading academics and practitioners, the centres are a keen theatre of debate, creating new thoughts, new ideas for both the theoretical study and practical application of accounting, finance and investment.

Programme structure

Learning will primarily be through lectures, set reading, class discussions, exercises, group-work assignments, problem solving in tutorials and case studies. Assessment methods include examinations, assignments, presentations or continuous assessment.

Learning outcomes

This programme enables students to analyse financial statements and to show the links between accounting statements, valuation methods and investment analysis.

Knowledge and understanding

Students will gain knowledge of global financial markets and the finance and investment industry – how different organisations interact, their roles, and factors behind success or failure. Students will learn how to estimate the fair value for an investment, to test assumptions and sensitivities, and to compare different investments.

Students will gain an understanding of the role of different asset classes, their behaviour in isolation and in relation to other asset classes, and an understanding of how portfolios of investments can be constructed and analysed.

Intellectual skills

Students will develop:

Critical analysis skills – an ability to assimilate new knowledge in the field of accounting and finance as well as the capacity to provide critical analysis of the field.
Research skills – an ability to identify and define pertinent research questions, to review the relevant literature, to define a proper methodology and to conduct research in the context of data analysis or experiments.
Discipline - a major difficulty in investment is removing emotion from the decision-making process. Study into behavioural finance shows that the desire of investors to follow consensus, and the ease with which they can misinterpret data, are obstacles to sound decision making. The programme will seek to imbue students with the discipline required to make good investment decisions.
Analytical and numerical skills - an ability to analyse and solve valuation and investment problems, to handle large volumes of numerical data and extract and manipulate relevant data in a meaningful manner.

Professional/subject-specific/practical skills

Students will develop:

An understanding of accounting, investment and risk management tools and databases such as Datastream, Reuters 3000Xtra, ThomsonOne Banker, CRSP, COMPUSTAT, London Share Price Database and WRDS.
An ability to: analyse and interpret financial data (such as financial statements); and to evaluate earnings quality and firm performance through individual and collaborative projects.
An understanding of analytical and problem-solving methods through the use of techniques such as discounted cash flow analysis.

Transferable skills

These include enhanced numerical skills and fluency in spreadsheet use and the ability to communicate challenging material both orally and in writing.

Read less
You will be provided with rigorous training in the analysis of issues in finance and corporate policy while improving your analytical and technical expertise. Read more
You will be provided with rigorous training in the analysis of issues in finance and corporate policy while improving your analytical and technical expertise. The programme is ideal for those whose career objectives lie broadly with the financial services and banking sectors. You will have the opportunity to gain an in depth grounding with core courses such as Foundations in Finance, Corporate Finance and Quantitative Methods in Finance, and subsequently tailor your programme to match your end goals through the range of optional courses on offer. These include Fixed Income Securities and Derivatives, Investment and Portfolio Management and Decision Theory and Behaviour amongst others.

You will be taught by a top-ranking Department of Economics with expertise in a broad range of areas, including people who have worked and are still working in the finance industry in the broad areas of asset allocation and risk, as well as algorithmic trading.

With a relatively small intake each year you will benefit from a strong sense of group identity and will enjoy close contact with the academic staff of the department. The course director and course coordinators serve as your personal advisors up until the spring, when you will then be assigned a personal dissertation supervisor.

The MSc Finance is an excellent preparation both for a career in the financial services, banking and business sectors and policy making, as well further academic study.

See the website https://www.royalholloway.ac.uk/economics/coursefinder/mscfinance.aspx

Why choose this course?

- The course offers an excellent opportunity to get a strong grounding in core areas of Finance and to specialise your knowledge further through the optional courses on offer.

-You will be taught by academics who produce world leading research some of whom are also currently working in the Finance and Banking sectors. In the 2008 Research Assessment Exercise we were ranked among the top 10 Economics Departments in the UK

- Students attend a two week pre-sessional quantitative methods course to ensure they are in a good position to start this challenging Masters courses

- The Department of Economics at Royal Holloway is unique in being a young department, created in 1995, in an established and prestigious college of the University of London.

- Our courses are small and select, thus ensuring that you will receive individual attention from the academic staff.

Department research and industry highlights

Economics is among the top departments in the UK for Research Excellence. In the 2008 Research Assessment Exercise (RAE), 80% of the Department's research submitted was ranked as world-leading or internationally excellent (rated 3* and 4*).

A recent analysis of the 2008 Research Assessment Exercise (RAE) shows that the Economics Department at Royal Holloway is ranked 8th best department in the UK for publications. The study by Jim Taylor and Ian Walker provides further insight into the research standing of UK economics departments. Previous rankings from the data already showed the Department in the top 10 in the UK.

We run a weekly Internal Seminar which provides a lively forum for work at an early stage of development. Our External Seminar Series runs weekly during term and during the last academic year, welcomed over 20 external speakers from prominent places. Invitees are the usual mixture of established names and newer entrants to the profession thought to be doing exciting work. Our Discussion Paper Series provides a forum for journal-ready work.

Course content and structure

You will study five core course units and, in addition, a mathematics refresher course and a dissertation, as well as choosing two elective course units.

Core course units:
- Pre-sessional mathematics refresher course
All students attend the compulsory pre-sessional mathematics refresher course, which runs for 2 weeks in September, before the start of term. There are no additional fees for this course however students will need to pay for accommodation for the period of this course.

- Corporate Finance
You will be introduced to the techniques of financial analysis and their applications to corporate finance. The concepts developed form the foundation of most elective finance course units. You will learn about the time value of money and the net present value rule, how to value financial assets, capital budgeting decisions, uncertainty and the risk-return trade-off and corporate governance.

- Quantitative Methods in Finance
This course unit will introduce you to mathematical statistics and theories that are applied in financial econometrics. The second half of the unit concerns the analysis of time series data including ARMA models, the analysis of non-stationary time series data, cointegration analysis, vector autoregressive models, modelling volatility in asset returns, forecasting and bootstrapping.

- Foundations of Finance
The course unit in finance will expose you to the structure of the financial markets, the instruments traded and the participants. You will be provided with the necessary tools with which to analyse how the financial markets function and how problems arise from their operations.

- Research Methods
While conducting research sounds like an easy task, it can present difficulties. This unit aims to help you avoid such traps and to assist you in developing strong research skills so that you can conduct an efficient piece of research at the end of your degree.

- Dissertation
The dissertation gives you the opportunity to analyse an economics issue in depth. You will be assigned a dissertation supervisor and, by the end of March, will submit a preliminary dissertation report that contains a clear statement of the problem under consideration, the structure of the project and the research methods that are going to be applied. The dissertation is then written over the summer.

Elective course units:
- Fixed Income Securities and Derivatives
You will gain an introduction to the alternative forms of financial assets that are traded in addition to stocks. Fixed income securities are bonds, bills and notes that offer a certain stream of income to holders. Derivatives are contingent and non-contingent claims on financial assets and are widely used for hedging risk. You will learn how to price these assets, and how to use them effectively in managing portfolios and hedging risk.

- Empirical Finance
The broad aims of this unit are to give you advanced-level training in evaluation of empirical models in finance. It will enable you to apply both quantitative techniques and qualitative methods, learnt elsewhere, to test theories and get acquainted with the existing literature in the field of finance.

- Investment and Portfolio Management
Underlying theory and empirical evidence in portfolio management will familiarise you with its practice in the finance sector. You will acquire an understanding of how funds are allocated when constructing a portfolio.

- Decision Theory and Behaviour
This course unit will deepen your knowledge of rational decision making through the exploration of behavioural models, their formalization and their scope, including applications to finance. You will also become familiarized with both theoretical and experimental methods for research in decision theory and behavioural economics.

- Public Economics
Public Economics is concerned with the study of the effects of government policy and the design of optimal policies. You will assess the implications of basic welfare economics in public policy. A number of recent research areas in public economics are then discussed including income taxation, tax evasion, externalities and social security.

- Political Economy
This course will provide an advanced treatment of the tools used in political economy to tackle some major questions faced by public sector economists. It will in particular focus on the modelling of voters and politicians’ behaviours to address the role played by incentives and constraints faced by politicians when choosing public policies. The effect of different forms of institutional arrangements on public decision making and electoral accountability will be analysed from both a theoretical and empirical perspective.

Assessment

Assessment is carried out by a variety of methods including coursework, examinations and a dissertation.

Employability & career opportunities

Our graduates are highly employable and, in recent years, have entered many different economics-related areas, including careers as economists, financial analysts, accountants, bankers, journalists and business analysts. Our graduates are currently working for firms such as Accenture, TNS, RBS, Deloitte, and Baker and McKenzie. At the same time, this course also equips you with a solid foundation for continued PhD studies. Your careers ambitions are supported by our College Careers Service, located right next door to the economics department. They offer application and interview coaching, career strategy discussions, and the opportunity to network with major finance employers on campus. Our careers service is provided by the Careers Group, the main provider of graduate recruitment services in London. Thus, you will have additional access to a wealth of presentations and networking opportunities which make the most of London’s financial centre.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online.

Read less
The Advanced Master in Quantitative Finance offers prospective students a rich curriculum combining finance, statistics, econometrics, programming and mathematics. Read more

Programme overview

The Advanced Master in Quantitative Finance offers prospective students a rich curriculum combining finance, statistics, econometrics, programming and mathematics. This Master guarantees a full coverage of financial disciplines, such as asset and derivative pricing, numerical methods and programming skills. This advanced course work is designed for students with a quantitative background, obtained either from recent education or through professional experience.

Programme objectives

The main objective of this master is to train a new generation of quants. You will gain cutting-edge knowledge in quantitative finance and will learn how to apply it to real-life problems.
You will not only be exposed to up-to-date models, but you will also understand their advantages and limitations, both in theory and in practice.

By the end of the year, you will be able to:

• Become an analyst and/or manager in a quant group
• Price equities and bonds
• Construct and programme structured products
• Perform tail-risk analysis
• Extract information from massive databases
• Understand and trade in complex derivative products such as volatility derivatives

Job opportunities

The obvious companies for such profiles are large financial institutions, private banking, and hedge funds, seeking to fulfil positions like quant team member, risk manager, quant analyst/risk modeller, asset liability manager, derivatives specialist, financial supervisor and product structure.

Geographically, job opportunities are not only concentrated to Europe, in particular London, Paris, Amsterdam, Frankfurt, and Zurich, but are extended to the rest of the world (e.g. Singapore, Hong Kong and New York).

However, to successfully grow in your professional life and make the right choices for your career, it is fundamental to define your goals and have the tools to achieve them. The Solvay Brussels School’s career service will help you in building your career thanks to its strong connection with the business sector and to its dynamic team who will accompany each participant into the professional world. From self-assessment to networking opportunities, our team will assist participants to realise their full potential. Through a full range of seminars, coaching sessions, workshops, events and other resources, we help our participants to discover the career path best suited to their personal fulfilment.

Interested? Visit http://am.solvay.edu/quantitative-finance/

Application Deadline

July 31st 2017
Non-EU citizens shall check visa procedure and length before applying.

Read less
This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science. Read more
This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science.

Degree information

The programme is designed to give students multidisciplinary skills in computing (i.e. programming, big data), analytics (i.e. data mining, machine learning, computational statistics, complexity), and business analysis. Emphasis will be on business problem framing, leveraging data as a strategic asset, and communicating complex analytical results to stakeholders.

Students undertake modules to the value of 180 credits. The programme consists of five core modules (90 credits), two optional modules (30 credits) and a dissertation (60 credits).

Core modules
-Programming for Business Analytics
-Data Analytics
-Information Retrieval and Data Mining
-Introduction to Supervised Learning
-Statistical NLP

Please note: the availability and delivery of modules may vary.

Optional modules
-Applied Machine Learning
-Graphical Models
-Web Economics
-Statistical Models and Data Analysis
-Statistical Design of Investigations
-Decision and Risk
-Consumer Behaviour and Behavioural Change
-Consulting Psychology
-Talent Management
-Data Science for Spatial Systems
-Group Mini Project: Digital Visualisation
-Urban Simulation
-Mastering Entrepreneurship
-Decision and Risk Analysis
-Managing Hi-Tech Organisations

Please note: the availability and delivery of modules may vary.

Dissertation/report
During the summer students will undertake a work placement with a UCL industrial partner. The research and data analysis conducted during this placement will form the basis of a 10,000-word dissertation.

Teaching and learning
The programme is delivered through a combination of lectures by world-class academics and industry leaders, seminars, workshops, tutorials and project work. The programme comprises two terms of taught material, followed by examinations and then a project. Assessment is through unseen written examinations, coursework and the dissertation.

Careers

Graduates of UCL Computer Science are particularly valued due to the department's international status, and strong reputation for leading research. Recent graduate destinations include: IBM, Samsung, Microsoft, Price Waterhouse Coopers, Citibank.

Employability
This programme is designed to satisfy the need, both nationally and internationally, for exceptional data scientists and analysts. Graduates will be highly employable in global companies and high-growth businesses, finance and banking organisations, major retail and service companies, and consulting firms. They will be equipped to influence strategy and decision-making, and be able to drive business performance by transforming data into a powerful and predictive strategic asset. We expect our graduates to progress to leading and influential positions in industry.

Why study this degree at UCL?

UCL Computer Science is a global leader in research in experimental computer science. The 2014 Research Excellence Framework (REF) ranked the department as first in the UK for research, with 96% regarded as internationally excellent.

The department consists of a team of world-class academics specialising in big data, computational statistics, machine learning and complexity.

The programme aims to create the next generation of outstanding academics and industry pioneers, who will use data analysis to deliver real social and business impact.

Read less
The MSc in Financial Services is a one-year full-time programme of study designed to equip students with the careful balance of academic knowledge and technical skills that are required for more high value-added positions available in the financial services industry in Ireland and abroad. Read more
The MSc in Financial Services is a one-year full-time programme of study designed to equip students with the careful balance of academic knowledge and technical skills that are required for more high value-added positions available in the financial services industry in Ireland and abroad.

Our graduates have found successful careers in the global finance industry as:

Financial Analyst
Risk Analyst
Credit Derivatives Analyst
Systems Specialist
Futures Trader
Price & Evaluations
Compliance Officer
Risk Manager
Asset Manager
Academic Researchers

Although primarily intended for business graduates, including recent graduates working in the financial services industry, the MSc will also appeal to graduates from quantitatively-oriented disciplines, such as engineering and the mathematical sciences, who have prior relevant training and/or academic knowledge in the field of financial services. The unique combination of subjects offered in the MSc facilitates subsequent professional accreditation within the international financial services industry.

We have pioneered the practical use of trading simulation within the University sector. This allows our students to actively manage a portfolio of stocks, bonds, futures and derivative instruments. This experience has given our students a significant advantage in understanding the dynamics of the marketplace.

Read less
In recent years, finance has been one of the areas where high-calibre mathematicians have been in great demand. Read more
In recent years, finance has been one of the areas where high-calibre mathematicians have been in great demand. With the advent of powerful and yet economically accessible computing, online trading has become a common activity, but many have realised that a certain amount of mathematics is necessary to be successful in such fields.

One of our most popular courses, MSc Mathematics and Finance allows those with a background in mathematics to study finance. Since finance routinely involves modelling and evaluating risk, asset pricing and price forecasting, mathematics has become an indispensable tool for this study.

You explore topics including:
-Models and mathematics in portfolio management
-Risk management in modern banking
-Financial modelling
-Actuarial modelling
-Applied statistics

Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines.

Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

This course can also be studied to a PGDip level - for more information, please view this web-page: http://www.essex.ac.uk/courses/details.aspx?mastercourse=PG00610&subgroup=2

Our expert staff

Our Department of Mathematical Sciences is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

Our staff have published several well-regarded text books and are world leaders in their individual specialisms, with their papers appearing in learned journals like Communications in Algebra, Studia Logica, International Journal of Algebra and Computation, SIAM Journal in Optimization, IEEE Evolutionary Computation, Computers and Operations Research, Ecology, Journal of Mathematical Biology, and Journal of Statistical Applications in Genetics and Molecular Biology.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-We host regular events and seminars throughout the year
-Our students run a lively Mathematics Society, an active and social group where you can explore your interest in your subject with other students

Your future

There is undoubtedly a shortage of mathematicians in general, and an even greater one of those with knowledge of finance.

Our course produces graduates with a sound background in mathematics and finance. Key employability skills include computing, use of algorithms, data analysis, mathematical modelling and understanding financial statements.

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Dissertation
-Research Methods
-Financial Modelling
-Mathematics of Portfolios
-Research Methods in Finance: Empirical Methods in Finance
-Stochastic Processes
-Applied Statistics (optional)
-Bank Strategy and Risk (optional)
-Bayesian Computational Statistics (optional)
-Combinatorial Optimisation (optional)
-Derivative Securities (optional)
-Economics of Financial Markets (optional)
-Financial Derivatives (optional)
-Ordinary Differential Equations (optional)
-Partial Differential Equations (optional)
-Statistical Methods (optional)
-Metric Spaces

Read less

  • 1
Show 10 15 30 per page


Share this page:

Cookie Policy    X