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Masters Degrees (Computational Finance)

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The master of science in computational finance is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of additional industries. Read more

Program overview

The master of science in computational finance is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of additional industries. Professionals in these fields use their strengths in business, modeling, and data analysis to understand and use complex financial models, often involving differential and stochastic calculus.

The program addresses a vital and growing career field, reaching beyond banking and finance. Typical job titles include risk analyst, research associate, quantitative analyst, quantitative structured credit analyst, credit risk analyst, quantitative investment analyst, quantitative strategist, data analyst, senior data analyst, fixed income quantitative analyst, and financial engineer. Computational finance is an excellent career option for technically-oriented professionals in the fields of business, math, engineering, economics, statistics, and computer science. Programming knowledge is highly preferred.

Plan of study

The curriculum offers an integration of finance, mathematics, and computing. The required mathematics courses have substantial financial content and the experiential computational finance course, which students take during the summer, makes use of skills learned in the mathematics, analytics, and finance courses taken up to that point. The program has a strong multidisciplinary nature and combines the expertise of four of RIT's colleges. The program is a full-time, 17-month curriculum beginning exclusively in the fall. The program ends with a required non-credit comprehensive exam based on the courses completed by the student.

Curriculum

Computational finance, MS degree, typical course sequence:
-Accounting for Decision Makers
-Survey of Finance
-Equity Analysis
-Debt Analysis
-Advanced Derivatives
-Mathematics for Finance I
-Mathematics for Finance II
-Analytics Electives
-Electives
-Computational Finance Experience

Other admission requirements

-Submit official transcripts (in English) from all previously completed undergraduate and graduate course work.
-Submit the results of the Graduate Management Admission Test (GMAT) or Graduate Record Exam (GRE) (GMAT preferred).
-Submit a personal statement (Applicants should explain why their background, please indicate mathematical and programming knowledge, and interests make them suitable for the program).
-Submit a current resume, and complete a graduate application.
-International applicants whose native language is not English must submit scores from the Test of English as a Foreign Language. Minimum scores of 580 (paper-based) or 92 (Internet-based) are required. Scores from the International English Language Testing System (IELTS) will be accepted in place of the TOEFL exam. The minimum acceptable score is 7.0. The TOEFL or IELTS requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions. For additional information on the IELTS, visit http://www.ielts.org.
-Completed applications for admission should be on file in the Office of Graduate Enrollment Services at least four weeks prior to registration for the next academic semester for students from the United States, and up to 10 weeks prior for international students applying for student visas.
-Accepted students can defer enrollment for up to one year. After one year, a new application must be submitted and will be re-evaluated based on the most current admission standards.

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This degree, offered jointly by the Department of Computer Science and the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling. Read more
This degree, offered jointly by the Department of Computer Science and the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling. This is an attractive advanced qualification, especially suitable if you are seeking employment in asset structuring, product pricing or risk management, among other fields.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/msccomputationalfinance.aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy, in particular of financial services and insurance. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will learn modern quantitative finance and computational methods for financial modeling. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world.

Department research and industry highlights

- The departments have expertise in a wide set of areas in Computer Science and in Economics, and the topics taught reflect these areas of excellence.

- Computer Science hosts one of strongest research groups in Machine Learning (the science of systems that learn from data).

- In the most recent Research Assessment Exercise (RAE 2008), Computer Science ranked 11th and Economics ranked 8th for their research output.

- Computer Science is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- Computer Science has an Industrial Liaison Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

On completion of the course graduates will have:
Throughout your degree, you will have the opportunity to acquire the following skills:

- Knowledge of the working of financial markets and their role in the context of global economy.
- Knowledge of modern mathematical and computational techniques used in finance.
- Knowledge of key ideas, principles, and methods of machine learning and their applications in finance.
- Ability to apply methods of computational finance to practical problems in computational finance, including pricing of derivatives and risk assessment.
- Ability to analyse and critically evaluate methods and general principles of computational finance and their applicability to specific problems; to critically evaluate validity and practicality of results.
- Ability to analyse and critically evaluate applicability of machine learning algorithms to problems in finance.
- Ability to implement methods of computational finance and machine learning using object-oriented programming languages and
modern data management systems.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.

Assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are highly employable and, in recent years, have entered many different finance-related areas, including careers as 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 both departments. 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 https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
This degree, offered jointly by the Department of Computer Science and the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling. Read more
This degree, offered jointly by the Department of Computer Science and the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling. This is an attractive advanced qualification, especially suitable if you are seeking employment in asset structuring, product pricing or risk management, among other fields.

As part of the course, you will take an industrial placement, where you will gain valuable experience by putting your knowledge and skills into practice.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/msccomputationalfinance(yearinindustry).aspx

Why choose this course?

- Big Data is now part of every sector and function of the global economy, in particular of financial services and insurance. Planning and strategic decision-making processes rely on large pools of data that need to be captured, aggregated, stored, and analysed.

- You will learn modern quantitative finance and computational methods for financial modeling. People with this set of skills are in short supply and high demand.

- You will have the opportunity to choose options among an exciting range of topics in Computer Science, Economics, Information Security, Management and Mathematics.

- Taking a placement is an excellent opportunity to gain industrial experience (which gives you an extra edge when applying for jobs in the future) and acquire skills that can only be fully picked up in a work environment.

- Industry connections have informed the content and design of the course. External contacts in both academia and industry enrich the programme of seminars and guest lectures, which are an integral part of the course.

- Royal Holloway is located in the ‘M4 corridor’, west of London, a major high-technology hub also called ‘England’s Silicon Valley’.

- Royal Holloway is a very prestigious university in which to study. We are ranked not only as one of the 16 most beautiful universities in the world, but also one of the best: in 2012/13, the Times Higher Education World University Rankings placed the College 15th in the UK, 45th in Europe and 119th in the world

Department research and industry highlights

- The departments have expertise in a wide set of areas in Computer Science and in Economics, and the topics taught reflect these areas of excellence.

- Computer Science hosts one of strongest research groups in Machine Learning (the science of systems that learn from data).

- In the most recent Research Assessment Exercise (RAE 2008), Computer Science ranked 11th and Economics ranked 8th for their research output.

- Computer Science is ranked third in the UK for graduate employability by the Times Good University Guide 2013.

- Computer Science has an Industrial Liaison Board that comprises senior representatives from Microsoft, Cognex, CSC, Bank of America Merrill Lynch, Kalido, Bathwick Group, Pentatonix, Blackrock, Oracle, Investec and QubeSoft.

Course content and structure

You will take taught modules during Term One (October to December) and Term Two (January to March). Examinations are held in May. You then take an industrial placement, after which you come back for your project/dissertation (12 weeks).

Your placement will take up to one year and, if you are an overseas student, your visa will cover the two years of the programme. The placement attracts a salary and is assessed as part of your degree. You will be assigned a supervisor by the host company, who is responsible for directing your work. You will be assigned an academic supervisor, who visits to check if you are integrating successfully and the type of work being undertaken is appropriate, and supports you in general during your placement. If you cannot or decide not to take a placement, you revert to the normal one-year degree.

On completion of the course graduates will have:

Throughout your degree, you will have the opportunity to acquire the following skills:

- Knowledge of the working of financial markets and their role in the context of global economy.
- Knowledge of modern mathematical and computational techniques used in finance.
- Knowledge of key ideas, principles, and methods of machine learning and their applications in finance.
- Ability to apply methods of computational finance to practical problems in computational finance, including pricing of derivatives and risk assessment.
- Ability to analyse and critically evaluate methods and general principles of computational finance and their applicability to specific problems; to critically evaluate validity and practicality of results.
- Ability to analyse and critically evaluate applicability of machine learning algorithms to problems in finance.
- Ability to implement methods of computational finance and machine learning using object-oriented programming languages and modern data management systems.
- Ability to work with software packages such as MATLAB and R.
- Knowledge of and ability to work with relational databases (SQL), non-relational databases (mongodb), as well as with Hadoop/pig scripting and other big data manipulation techniques.
- Knowledge of and ability to work with Python, Perl, and Shell Scripting.
- Work experience and appreciation of how your work fits into the organizational and development processes of a company.

Assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Employability & career opportunities

Our graduates are highly employable and, in recent years, have entered many different finance-related areas, including careers as 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 both departments. 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 https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

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This one year MSc programme in Statistics and Computational Finance aims to train students to work as professional statisticians, not only at the interface between statistics and finance, but to provide skills applicable in sociology, health science, medical science, biology, and other scientific areas where data analysis is needed. Read more
This one year MSc programme in Statistics and Computational Finance aims to train students to work as professional statisticians, not only at the interface between statistics and finance, but to provide skills applicable in sociology, health science, medical science, biology, and other scientific areas where data analysis is needed.

The emphasis of the programme is on data analysis. It equips students with contemporary statistical ideas and methodologies as well as advanced knowledge, which will make students very competitive to industry, academic and governmental institutions. There are excellent career prospects for employment in industry and the public sector for our graduates. An MSc degree in Statistics and Computational Finance provides attractive employment opportunities in financial industries, government, consultancy companies, research centres, and other industries where data analysis is needed. Students with an interest in academic work may also decide to continue on a PhD programme in Statistics or a related field, for which the MSc in Statistics and Computational Finance provides a sound foundation.

Career opportunities

There are excellent career prospects for students with a background in statistics and data analysis. The programme is designed to equip students with contemporary statistical ideas and methodologies which makes our students very competitive when seeking employment in industry and governmental institutions, as well as in academic careers. The skills taught are applicable in sociology, health science, medical science, biology and other related disciplines where data analysis is needed.

Recent destinations of graduates from the MSc in Statistics and Computational Finance have included:
-PhD in the Department of Mathematics at the University of York (Non-parametric modelling in high dimensional data analysis)
-PhD at Florida State University
-Modelling Analyst (automotive data provider)
-Graduate Technical Analyst (HSBC)
-Research and Development in a Property and Casualty Insurance company, specialising in catastrophe insurance
-Mainframe Software Solution Sales in a major IT brand
-Data Analyst in a health data company
-Trainee Chartered Accountant

Programme structure

To achieve an MSc degree students must complete modules to the value of 180 credits, including 100 credits of core taught modules, 20 credits chosen among the optional taught modules, and a 60-credit dissertation.

Students who successfully complete 60 credits of taught modules may be eligible for the award of a Postgraduate Certificate.

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Our MSc Computational Finance equips you with the core concepts and mathematical principles of modern quantitative finance, plus the operational skills to use computational packages (mainly Matlab) for financial modelling. Read more
Our MSc Computational Finance equips you with the core concepts and mathematical principles of modern quantitative finance, plus the operational skills to use computational packages (mainly Matlab) for financial modelling.

We provide practical, hands-on learning about how modern, highly computerised financial markets work, how assets should be priced, and how investors should construct a portfolio of assets. In addition to traditional topics in derivatives and asset pricing, we place a special emphasis on risk management in non-Gaussian environment with extreme events.

You master these areas through studying topics including:
-Non-linear and evolutionary computational methods for derivatives pricing and portfolio management
-Applications of calculus and statistical methods
-Computational intelligence in finance and economics
-Financial markets

You also graduate with an understanding of the use of artificial financial market environments for stress testing, and the design of auctions and other financial contracts.

Our Centre for Computational Finance and Economic Agents is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation.

Our research is geared towards real-world, practical applications, and many of our academic staff have experience of applying their findings in industry and in advising the UK government.

This course is also available on a part-time basis.

Professional accreditation

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

This course is taught by experts with both academic and industrial expertise in the financial and IT sectors. We bring together leading academics in the field from our departments of economics, computer science and business.

Our staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management.

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors

Your future

We have an extensive network of industrial contacts through our City Associates Board and our alumni, while our expert seminar series gives you the opportunity to work with leading figures from industry.

Our recent graduates have gone on to become quantitative analysts, portfolio managers and software engineers at various institutions, including:
-HSBC
-Mitsubishi UFJ Securities
-Old Mutual
-Bank of England

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-CCFEA MSc Dissertation
-Financial Engineering and Risk Management
-Introduction to Financial Market Analysis
-Learning and Computational Intelligence in Economics and Finance
-Professional Practice and Research Methodology
-Quantitative Methods in Finance and Trading
-Big-Data for Computational Finance (optional)
-Industry Expert Lectures in Finance (optional)
-Mathematical Research Techniques Using Matlab (optional)
-Programming in Python (optional)
-Artificial Neural Networks (optional)
-High Frequency Finance and Empirical Market Microstructure (optional)
-Machine Learning and Data Mining (optional)
-Trading Global Financial Markets (optional)
-Creating and Growing a New Business Venture (optional)
-Evolutionary Computation and Genetic Programming (optional)
-Constraint Satisfaction for Decision Making (optional)

Read less
Our MSc Computational Finance equips you with the core concepts and mathematical principles of modern quantitative finance, plus the operational skills to use computational packages (mainly Matlab) for financial modelling. Read more
Our MSc Computational Finance equips you with the core concepts and mathematical principles of modern quantitative finance, plus the operational skills to use computational packages (mainly Matlab) for financial modelling.

We provide practical, hands-on learning about how modern, highly computerised financial markets work, how assets should be priced, and how investors should construct a portfolio of assets. In addition to traditional topics in derivatives and asset pricing, we place a special emphasis on risk management in non-Gaussian environment with extreme events.

You master these areas through studying topics including:

- Non-linear and evolutionary computational methods for derivatives pricing and portfolio management
- Applications of calculus and statistical methods
- Computational intelligence in finance and economics
- Financial markets

You also graduate with an understanding of the use of artificial financial market environments for stress testing, and the design of auctions and other financial contracts.

Our Centre for Computational Finance and Economic Agents is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation.

Our research is geared towards real-world, practical applications, and many of our academic staff have experience of applying their findings in industry and in advising the UK government.

Read less
The MSc in Computational Finance is an intensive 12 month, full-time programme which combines theoretical rigour with a practical emphasis on the acquisition of advanced computational finance skills. Read more
The MSc in Computational Finance is an intensive 12 month, full-time programme which combines theoretical rigour with a practical emphasis on the acquisition of advanced computational finance skills. It is this blend of advanced analytical and quantitative skills, combined with the innovative use of experiential-learning techniques such as simulated 'live' financial markets trading workshops, which will differentiate graduates of this programme from other programmes in the field.

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The MSc Computational Finance will provide you with mathematical and computational skills required to solve real problems in quantitative finance. Read more
The MSc Computational Finance will provide you with mathematical and computational skills required to solve real problems in quantitative finance. Many areas of modern finance such as risk management and option pricing emphasise numerical and computational skills as well as an understanding of the mathematical background.

The programme brings together expertise from Mathematics and the Business School to ensure a balanced approach to many of the complex problems in modern quantitative finance.

On completion of the programme you will be able to review and implement complex financial models in a number of programming languages including C++, MATLAB and R.

Programme structure

Core modules

The compulsory modules can include; Methods for Stochastics and Finance; Analysis and Computation for Finance; Mathematical Theory of Optional Pricing; Introduction to C++; Computational Finance with C++; Numerical Finance; Research Methodology; Advanced Mathematics Project; Investment Analysis I; Investment Analysis II; Financial Modeling

Optional modules

Some examples of the optional modules are as follows; Topics in Financial Economics; Banking and Financial Services; Derivatives Pricing; Domestic and International Portfolio Management; Advanced Corporate Finance; Alternative Investments; Quantitative Research Techniques; Advanced Econometrics;

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Students develop an advanced knowledge of computational methods in finance, which is a prerequisite for a successful career in the financial industry within 'quant' teams. Read more
Students develop an advanced knowledge of computational methods in finance, which is a prerequisite for a successful career in the financial industry within 'quant' teams. 'Quants' (development analysts) design and implement complex models and are sought after by banks, fund managers, insurance companies, hedge funds, and financial software and data providers.

Degree information

This degree comprises advanced modules on quantitative and modelling skills, which are essential for 'quant' roles in trading research, regulation and risk. This applied MSc programme is distinctive in that it provides a solid mathematical and statistical foundation together with an education in advanced-level programming.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a dissertation (60 credits).

Core modules
-Market Risk Measures and Portfolio Theory
-Numerical Analysis for Finance
-Financial Data and Statistics
-Financial Market Modelling and Analysis

Optional modules
-Operational Risk Measurement for Financial Institutions
-Stochastic Processes for Finance
-Financial Engineering
-Supervised Learning
-Programming & Mathematical Methods for Machine Learning
-Financial Institutions and Markets
-Networks and Systemic Risk
-Market Microstructure
-Algorithmics
-Database Systems
-Software Engineering
-Applied Computational Finance

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of about 10,000 words or 50 pages. Usually this will be undertaken during a summer placement in an industry environment arranged by the department.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials, seminars, and project work. It comprises two terms of teaching, followed by examinations an a dissertation. Assessment is through coursework, unseen examinations and a dissertation.

Careers

This is a relatively new programme and therefore no information on graduate destinations is currently available. UCL Computer Science graduates typically find work in financial institutions such as Credit Suisse, JP Morgan, Morgan Stanley, and Deutsche Bank as financial analyst application developers, quant developers, and business managers. The University of Cambridge and UCL are among top further study destinations.

Employability
Our graduates are particularly valued as a result of the department's international reputation, strong links with industry, and ideal location close to the City of London. Graduates are especially sought after by leading finance companies and organisations.

Why study this degree at UCL?

UCL was ranked first in the UK for computer science and informatics in the Research Excellence Framework (REF) 2014. UCL Computer Science hosts the Doctoral Training Centre in Financial Computing and Analytics, which is the only one of its kind in the UK. UCL's central London location ideally places it close to one of the world's most important financial centres, with which UCL pioneers industrial/academic engagements. Students on the Computational Finance MSc will benefit from teaching input from City of London practitioners.

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This programme is designed for graduates in mathematics, engineering, computer science and finance/economics wishing to pursue careers in the financial services and banking industry. Read more
This programme is designed for graduates in mathematics, engineering, computer science and finance/economics wishing to pursue careers in the financial services and banking industry. The structure is of an interdisciplinary nature in which graduates coming from different disciplines collaborate to address the computational aspects of market risk. Our core philosophy is to equip our students with the appropriate knowledge in mathematical finance, focusing on a strong development of associated computational methods.

Our Royal Maritime (Heritage) London based campus, close to the financial district of Canary Wharf, enables the department to build ties with market practitioners permitting our students to become part of a wider financial group. Our seminar series, inviting both academics and practitioners, allows you to interact with our external links creating an advantageous learning experience. We provide the knowledge for you to build up your profile of understanding of current research practice in finance. You will be trained and equipped with the skills for derivatives pricing and make use of non-linear methods for quantitative analysis (programming in Matlab, R and VBA). Our classes include interactive applications that enhance your learning experience through innovative teaching. By utilising research expertise within the department you will graduate with a strong understanding of numerical methods. You will also develop an understanding for further applicability in relevant fields as in energy commodity markets, where part of our current research focuses on by combining the world leading Agent-Based research team with our Computational Finance applications for crude oil price modelling.

The programme welcomes both recent graduates as well as experienced professional practitioners who wish to further their skills. Programme assessments are all 100% coursework with problems relating to current market practice. A supervised dissertation project takes place after the end of the last teaching term during the summer months. Projects are allocated in March and students are encouraged to work on projects that provide genuine insight in financial markets analysis. The programme is also available on a part-time basis. For those already at employment the flexible part-time mode of study, two years typically but can be flexible, allows students to be committed to both the MSc programme and employment.

Visit the website http://www2.gre.ac.uk/study/courses/pg/maths/compfinance

Mathematics

Postgraduate mathematics students benefit from award-winning teaching and great facilities. Our programmes are informed by world-renowned research and our links with industry ensure our students develop the academic and practical skills that will enhance their career prospects.

What you'll study

Full time
- Year 1:
Students are required to study the following compulsory courses.

English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)
Financial Markets (Dual Award) (15 credits)
Masters Project (Maths) (60 credits)
Advanced Finite Difference Methods for Derivatives Pricing (15 credits)
Computational Methods (15 credits)
Mathematical Approaches to Risk Management (15 credits)
Mathematical Finance (30 credits)

Students are required to choose 15 credits from this list of options.

Scientific Software Design and Development (15 credits)
Inverse Problems (15 credits)

Students are required to choose 15 credits from this list of options.

Enterprise Software Engineering Development (15 credits)
Software Tools and Techniques (15 credits)
Actuarial Mathematics and Risk Modelling (15 credits)
Financial Time Series (15 credits)

Part time
- Year 1:
Students are required to study the following compulsory courses.

Computational Methods (15 credits)
Inverse Problems (15 credits)
Mathematical Finance (30 credits)

- Year 2:
Students are required to study the following compulsory courses.

Scientific Software Design and Development (15 credits)
Financial Markets (Dual Award) (15 credits)
Masters Project (Maths) (60 credits)
Advanced Finite Difference Methods for Derivatives Pricing (15 credits)
Mathematical Approaches to Risk Management (15 credits)

Fees and finance

Your time at university should be enjoyable and rewarding, and it is important that it is not spoilt by unnecessary financial worries. We recommend that you spend time planning your finances, both before coming to university and while you are here. We can offer advice on living costs and budgeting, as well as on awards, allowances and loans.

Find out more about our fees and the support available to you at our:
- Postgraduate finance pages (http://www.gre.ac.uk/finance/pg)
- International students' finance pages (http://www.gre.ac.uk/finance/international)

Assessment

100% coursework. Coursework assessment at the postgraduate level allows for better elaboration of ideas and expansion of knowledge. A supervised dissertation project takes places at the end of the teaching terms during the summer months. The Department is very keen to tackle dissertation topics.

Career options

Graduates are equipped with the tools needed to become quantitative analysts, work in risk and portfolio management as well as in the insurance sector. Our expert seminar series gives you the opportunity to interact with leading figures from industry and academia and undertake projects relating to current industry practice. A postgraduate qualification is a major achievement and a milestone in your specialised career path leading to a professional career. The Department also offers a PhD programme.

Find out how to apply here - http://www2.gre.ac.uk/study/apply

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The programme will equip students with the cutting-edge quantitative and computational techniques and strategies utilised by leading financial firms and will prepare students for future careers in a quantitative finance, trading or more general finance environment. Read more
The programme will equip students with the cutting-edge quantitative and computational techniques and strategies utilised by leading financial firms and will prepare students for future careers in a quantitative finance, trading or more general finance environment.

Key feature: a number of core modules will be taught in the School's new First Derivatives Trading Room, which recreates the excitement of a financial trading floor, and provides students with hands-on trading and investment management experience. Real-time data displayed using industry-standard financial technologies (Bloomberg terminals) will be used to provide the best possible introduction to a financial trading environment.

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The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of real finance problems. … Read more

The course provides you with a strong mathematical background with the skills necessary to apply your expertise to the solution of real finance problems. You will develop skills so that you are able to formulate a well posed problem from a description in financial language, carry out relevant mathematical analysis, develop and implement an appropriate numerical scheme and present and interpret these results.

The course lays the foundation for further research in academia or for a career as a quantitative analyst in a financial or other institution.

You will take three introductory courses in the first week. The introductory courses cover partial differential equations, probability and statistics and MATLAB.

The first term focuses on compulsory core material, offering 80 hours of lectures and 40 hours of classes/practical. The core courses are as follows:

  • Stochastic Calculus
  • Financial Derivatives
  • Numerical Methods I - Monte-Carlo
  • Numerical Methods I - Finite Differences
  • Statistics and Financial Data Analysis
  • Financial Programming with C++ 1

In the second term, three streams are offered; each stream consists of 32 hours of lectures and 16 hours of classes/practical. The Tools stream is mandatory and you will also take either the Modelling stream or the Data-driven stream.

Modelling stream

  • Exotic derivatives
  • Stochastic volatility, jump diffusions
  • Commodities
  • Fixed income

Data-driven stream

  • Asset pricing and inefficiency of markets
  • Market microstructure and trading
  • Algorithmic trading
  • Advanced financial data analysis
  • Machine learning
  • Python

Tools stream

  • Numerical methods 2 - Monte Carlo methods
  • Numerical methods 2 - Finite differences
  • Calibration
  • Optimisation
  • Introduction to stochastic control

As well as the streams, the course includes a compulsory one-week (24 hours of lectures) intensive module on quantitative risk management which is to be held in/around the week before the third term.

The third term is dedicated to a dissertation project which is to be written on a topic chosen in consultation with your supervisor.

The second component of the financial computing course, Financial Computing with C++ 2 (24 hours of lectures and practicals in total), is held shortly after the third term.

The examination will consist of the following elements:

  • two written examinations and one take-home project, each of two hours' duration - the written examinations will cover the core courses in mathematical methods and numerical analysis
  • a written examination on the Modelling stream or a written examination and a computer-based practical examination on the Data-driven stream
  • a written examination assessing the Tools stream
  • a take-home project assessing the course in quantitative risk management
  • two practical examinations assessing two courses in financial computing with C++.

Graduate destinations

MSc graduates have been recruited by prominent investment banks and hedge funds. Many past students have also progressed to PhD-level studies at leading universities in Europe and elsewhere.



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This Masters degree provides you with knowledge of advanced finance concepts, whilst developing your quantitative, mathematical and research skills. Read more

This Masters degree provides you with knowledge of advanced finance concepts, whilst developing your quantitative, mathematical and research skills.

Taught by experienced academics based in both Leeds University Business School and the School of Mathematics, you’ll cover key topics including financial derivative pricing, discrete and continuous time models, risk management and portfolio optimisation, as well as statistical methods for finance.

You will be equipped with a rare combination of mathematical skills and the latest business finance knowledge, which is highly sought after in the financial sector by banks, investment and consultancy companies. It’s also excellent preparation if you’re interested in pursuing further academic research.

This course is ideal if you’ve previously studied finance, economics, mathematics, physics or computing, and are interested in applying your skills to financial markets.

Academic excellence

As a student, you will be able to access the knowledge of our advanced specialist research units, which also have strong links with leading institutions in the US, Europe and Asia. These include the Centre for Advanced Study in Finance (CASIF), the Institute of Banking and Investment (IBI) and the Credit Management Research Centre (CMRC).

This research makes an important contribution to your learning on the MSc Financial Mathematics; you will benefit from a curriculum that is informed by the latest knowledge and critical thinking.

You will also benefit from our strong relationships with the finance, credit and accounting professions. This provides a connection to the latest practitioner and policy developments, giving you a masters degree that is relevant to the contemporary environment.

Course content

In your first semester you’ll develop a broad understanding of corporate finance and how financial theory relates to practice in business and financial markets. This will put your mathematical studies into context while you develop your skills in applied statistics and probability, optimisation methods and discrete time finance.

You’ll build on these skills in topics such as continuous time finance, risk management and computational methods. You’ll also gain specialist knowledge in topics that suit your career ambitions such as risk and insurance, actuarial science and behavioural finance.

The programme will improve your research skills and allow you to study different research methodologies, including those employed by our own leading academics. This will prepare you for your dissertation – an independent research project on a topic of your choice that you’ll submit by the end of the year.

Course structure

Compulsory modules

  • Corporate Finance 15 credits
  • Dissertation in Financial Mathematics 30 credits
  • Applied Statistics and Probability 15 credits
  • Discrete Time Finance 15 credits
  • Continuous Time Finance 15 credits
  • Risk Management 15 credits
  • Computations in Finance 15 credits
  • Optimisation Methods for Finance 15 credits

Optional modules

You'll also take two optional modules.

  • Security Investment Analysis 15 credits
  • Portfolio Risk Management 15 credits
  • Behavioural Finance 15 credits
  • Financial Derivatives 15 credits
  • International Investment 15 credits
  • Models in Actuarial Science 15 credits

For more information on typical modules, read Financial Mathematics MSc in the course catalogue

Learning and teaching

We use a variety of teaching and learning methods to help you make the most of your studies. These will include lectures, seminars, workshops, online learning and tutorials. Independent study is also vital for this course allowing you to prepare for taught classes and sharpen your own research and critical skills.

In addition to the assessed modules and research dissertation, you benefit from professional training activities and employability workshops. Thanks to our links with major companies across the business world, you can also gain a practical understanding of key issues.

Recent activities have included CV building and interview sessions, professional risk management workshops and commercial awareness events. For example, students have developed their knowledge of financial markets through a one-week trading simulation. Read more about professional development activities for postgraduate finance students.

Assessment

Assessment methods emphasise not just knowledge, but essential skills development too. They include formal exams, group projects, reports, computer simulation exercises, essays and written assignments, group and individual presentations.

This diversity enables you to develop a broad range of skills as preparation for professional life.

Career opportunities

You have various opportunities open to you as a Financial Mathematics graduate, including: quantitative analysis, risk management, investment banking, financial consultancy, insurance, accounting and academia.

Previous graduates have gone on to secure employment with Allianz (London), AstraZeneca, Barclays, Cathay Life Insurance, CITIC Group, Commerzbank, Deloitte, First Direct, Gaz de France, HSBC, KPMG, Moody’s, PricewaterhouseCoopers, Royal Bank of Scotland, RSA and UK Government Actuary’s Department.

Careers support

We help you to achieve your career ambitions by providing professional development support and training as part of the course. You benefit from the support of a professional development tutor, who will work with you to develop the important professional skills that employers value.

Read more about our careers and professional development support.

The University of Leeds Careers Centre also provides a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website



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The financial services industry place great emphasis on raising the level of mathematics used in banks in applications to pricing, hedging and risk management. Read more
The financial services industry place great emphasis on raising the level of mathematics used in banks in applications to pricing, hedging and risk management. This MSc provides students with the skills necessary in mathematics, statistics and computation for a career in this fast-developing field.

Degree information

Students will develop a detailed understanding of the application of mathematics, statistics and computation to problems in finance, and will gain the necessary practical tools for the pricing, hedging and risk management of a diverse range of financial products in several asset classes.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits) four optional modules (60 credits) and a research dissertation (60 credits).

A Postgraduate Diploma will be offered to the students that have completed 8 taught modules (120 UCL credits).A Postgraduate Certificate will be offered to the students that have completed 4 taught modules (60 UCL credits).

Core modules
-Asset Pricing in Continuous Time
-Forecasting
-Interest Rates and Credit Modelling
-Quantitative and Computational Finance

Optional modules - four modules must be chosen from the following list:
-Applied Computational Finance
-Equities, Foreign Exchange and Commodities Modelling
-Market Risk, Measures and Portfolio Theory
-Mathematics and Statistics of Algorithmic Trading
-Numerical Analysis for Finance
-Probability
-Statistical Inference
-Stochastic Processes
-Quantitative Modelling of Operational Risk and Insurance Analytics

Dissertation/report
All MSc students undertake an independent research project, which culminates in a research report of approximately 10,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, practical classes, tutorials and problem-solving exercises. Assessment is through written papers, coursework, examinations and the research report and presentation.

Careers

Many students have progressed to careers in financial services in the City of London or in their home country; a number of graduates have proceeded to a PhD.

Top career destinations for this degree:
-University Teacher, Chechen State University
-Operational Permanent Control Analyst, BNP Paribas
-UK Education Consultant, SI-UK Education Council
-CFA (Chartered Financial Analyst), Quartic Training
-MSc Financial Mathematics, University College London (UCL)

Employability
The financial services industry requires quantitative finance professionals who are able to analyse data, to program, and who are expert in mathematics and computational statistics. Career prospects for graduates of this programme are excellent.

Why study this degree at UCL?

UCL Mathematics is an internationally renowned department which carries out excellent individual and group research applying modelling techniques to problems in financial, industrial, biological and environmental areas.

The department hosts a stream of distinguished international visitors. In recent years four staff members have been elected fellows of the Royal Society, and the department publishes the highly regarded research journal Mathematika.

A notable aspect of this applied Master's programme is that students will be educated to an advanced level in statistics and computing.

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On our MSc Algorithmic Trading, we equip you with the core concepts and quantitative methods in high frequency finance, along with the operational skills to use state-of-the-art computational methods for financial modelling. Read more
On our MSc Algorithmic Trading, we equip you with the core concepts and quantitative methods in high frequency finance, along with the operational skills to use state-of-the-art computational methods for financial modelling.

We enable you to attain an understanding of financial markets at the level of individual trades occurring over sub-millisecond timescales, and apply this to the development of real-time approaches to trading and risk-management.

The course includes hands-on projects on topics such as order book analysis, VWAP & TWAP, pairs trading, statistical arbitrage, and market impact functions. You have the opportunity to study the use of financial market simulators for stress testing trading strategies, and designing electronic trading platforms.

In addition to traditional topics in financial econometrics and market microstructure theory, we put special emphasis on areas:
-Statistical and computational methods
-Modelling trading strategies and predictive services that are deployed by hedge funds
-Algorithmic trading groups
-Derivatives desks
-Risk management departments

Our Centre for Computational Finance and Economic Agents is an innovative and laboratory-based teaching and research centre, with an international reputation for leading-edge, interdisciplinary work combining economic and financial modelling with computational implementation. We are supported by Essex’s highly rated Department of Economics, School of Computer Science and Electronic Engineering, and Essex Business School.

We are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of our research rated ‘world-leading’ or ‘internationally excellent (REF 2014).

Professional accreditation

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

This course is taught by experts with both academic and industrial expertise in the financial and IT sectors. We bring together leading academics in the field from our departments of economics, computer science and business.

Our staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management.

More broadly, our research covers a range of topics, from materials science and semiconductor device physics, to the theory of computation and the philosophy of computer science, with most of our research groups based around laboratories offering world-class facilities.

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors

Your future

We have an extensive network of industrial contacts through our City Associates Board and our alumni, while our expert seminar series gives you the opportunity to work with leading figures from industry.

Our recent graduates have gone on to become quantitative analysts, portfolio managers and software engineers at various institutions, including:
-HSBC
-Mitsubishi UFJ Securities
-Old Mutual
-Bank of England

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-CCFEA MSc Dissertation
-Big-Data for Computational Finance
-High Frequency Finance and Empirical Market Microstructure
-Introduction to Financial Market Analysis
-Professional Practice and Research Methodology
-Quantitative Methods in Finance and Trading
-Trading Global Financial Markets
-Cloud Technologies and Systems (optional)
-Constraint Satisfaction for Decision Making (optional)
-Creating and Growing a New Business Venture (optional)
-Digital Signal Processing (optional)
-Evolutionary Computation and Genetic Programming (optional)
-Financial Engineering and Risk Management (optional)
-High Performance Computing (optional)
-Industry Expert Lectures in Finance (optional)
-Learning and Computational Intelligence in Economics and Finance (optional)
-Mathematical Research Techniques Using Matlab (optional)
-Programming in Python (optional)
-Text Analytics (optional)

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