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Masters Degrees in Stochastic Processes

We have 53 Masters Degrees in Stochastic Processes

Masters degrees in Stochastic Processes focus on systems and processes involving random variables.

Related subjects include Financial Mathematics, Scientific Computation and Applied Stochastics. Entry requirements typically include an appropriate undergraduate degree in a relevant mathematical discipline.

Why study a Masters in Stochastic Processes?

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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Stochastic Processes. Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Stochastic Processes: Theory and Application at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

The MRes in Stochastic Processes: Theory and Application is delivered through optional modules for the taught element followed by a large research project that contributes to the field in an explicit way, rather than merely applying existing knowledge.

The Department of Mathematics hosts one of the strongest research groups in probability theory, especially in stochastic processes, in the UK. The senior members of this group are world leaders in their fields.

The Department’s research groups include:

Algebra and Topology Group

Areas of interest include: Noncommutative geometry, Categorical methods in algebra and topology, Homotopy theory and homological algebra and others.

Analysis and Nonlinear Partial Differential Equations Group

Areas of interest include: Reaction-diffusion and reaction-diffusion-convection equations and systems, Navier–Stokes equations in fluid dynamic, Complexity in the calculus of variations and others.

Stochastic Analysis Group

Areas of interest include: Functional inequalities and applications, Lévy-type processes, Stochastic modelling of fractal, multi-fractal and multi-scale systems, Infinite dimensional stochastic analysis and others.

Mathematical Methods in Biology and Life Sciences Group

Areas of interest include: Mathematical pharmacology; heat and mass transfer models for plant cooling; modelling cellular signal transduction dynamics; mathematical oncology: multi-scale modelling of cancer growth, progression and therapies, and modelling-optimized delivery of multi-modality therapies; multi-scale analysis of individual-based models; spreading speeds and travelling waves in ecology; high performance computing.

Key Features

The Department of Mathematics hosts one of the strongest research groups in probability theory, especially in stochastic processes, in the UK. The senior members of this group are world leaders in their fields.

Course Content

As a student on the MRes Stochastic Processes programme you will study a range of topics for the taught element including:

Stochastic Calculus based on Brownian Motion

Levy processes and more general jump processes

The advanced Black-Scholes theory

Theory and numerics of parabolic differential equations

Java programming

The Stochastic Processes: Theory and Application course consists of a taught part (60 credits) and a research project (120 credits). Students will have a personal supervisor for their research project from the start of their studies.

Research projects could be of a theoretical mathematical nature, or they could be more applied, for example in financial mathematics or actuarial studies. Some of the research projects will be of an interdisciplinary character in collaboration with some of Swansea's world class engineers. For such projects it is likely that EPSRC funding would be available.

Facilities

The Aubrey Truman Reading Room, located in the centre of the Department of Mathematics, houses the departmental library and computers for student use. It is a popular venue for students to work independently on the regular example sheets set by their lecturers, and to discuss Mathematics together.

Our main university library, Information Services and Systems (ISS), contains a notably extensive collection of Mathematics books.

Careers

The ability to think rationally and to process data clearly and accurately are highly valued by employers. Mathematics graduates earn on average 50% more than most other graduates. The most popular areas are the actuarial profession, the financial sector, IT, computer programming and systems administration, and opportunities within business and industry where employers need mathematicians for research and development, statistical analysis, marketing and sales.

Some of our students have been employed by AXA, BA, Deutsche Bank, Shell Research, Health Authorities and Local Government. Teaching is another area where maths graduates will find plenty of career opportunities.

Research

The results of the Research Excellence Framework (REF) 2014 show that our research environment (how the Department supports research staff and students) and the impact of our research (its value to society) were both judged to be 100% world leading or internationally excellent.

All academic staff in Mathematics are active researchers and the department has a thriving research culture.



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About the MSc programme. The programme draws on LSE's strengths in finance and related areas to provide high-level instruction in the mathematical theory underlying finance, and training in appropriate computational methods. Read more

About the MSc programme

The programme draws on LSE's strengths in finance and related areas to provide high-level instruction in the mathematical theory underlying finance, and training in appropriate computational methods.

The MSc Financial Mathematics is based in the Department of Mathematics, and is taught in collaboration with the Department of Finance and the Department of Statistics.

The programme aims to develop your understanding of quantitative methodologies and techniques that are important for a range of jobs in investment banks and other financial institutions; to enhance your critical appreciation of major issues and emerging theory in the area of financial mathematics; and to improve your personal skills, including logical reasoning, quantitative analysis and the presentation of technical results.

In addition to compulsory courses in The Mathematics of the Black and Scholes Theory, The Foundations of Interests Rate and Credit Risk Theory, Stochastic Processes, Fixed Income Markets, and Computational Methods in Finance, you will choose optional courses to the value of one and a half units. Choices include stochastic analysis, preferences, optimal portfolio choice, equilibrium, derivatives modelling, Markov processes, financial risk analysis, international finance, and forecasting of financial time series.

Graduate destinations

This programme is ideal preparation for a range of careers in the financial sector, industry and research.

Further information on graduate destinations for this programme



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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Mathematics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Mathematics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

As an MSc by Research in Mathematics student you will be guided by internationally leading researchers and will carry out a large individual research project.

You will be fully integrated into one of our established research groups and participate in research activities such as seminars, workshops, laboratories, and field work.

Key Features

Swansea is a research-led University and the Mathematics Department makes a significant contribution, meaning that as a postgraduate Mathematics student you will benefit from the knowledge and skills of internationally renowned academics.

In the Department of Mathematics at Swansea you will find friendly teaching staff that are fully committed to providing you with a supportive teaching and learning environment. This includes outstanding student support.

All postgraduate Mathematics programmes at Swansea will equip you with skills relevant for a rewarding career in a range of diverse fields. You will also further develop your communication, presentation and analytical skills.

The Mathematics Department’s research groups include:

Algebra and Topology Group

Areas of interest include: Noncommutative geometry, Categorical methods in algebra and topology, Homotopy theory and homological algebra and others.

Analysis and Nonlinear Partial Differential Equations Group

Areas of interest include: Reaction-diffusion and reaction-diffusion-convection equations and systems, Navier–Stokes equations in fluid dynamic, Complexity in the calculus of variations and others.

Stochastic Analysis Group

Areas of interest include: Functional inequalities and applications, Lévy-type processes, Stochastic modelling of fractal, multifractal and multiscale systems, Infinite dimensional stochastic analysis and others.

Mathematical Methods in Biology and Life Sciences Group

Areas of interest include: Mathematical pharmacology; heat and mass transfer models for plant cooling; modelling cellular signal transduction dynamics; mathematical oncology: multi-scale modelling of cancer growth, progression and therapies, and modelling-optimized delivery of multi-modality therapies; multi-scale analysis of individual-based models; spreading speeds and travelling waves in ecology; high performance computing

Employability

The ability to think rationally and to process data clearly and accurately are highly valued by employers. Mathematics graduates earn on average 50% more than most other graduates. The most popular areas are the actuarial profession, the financial sector, IT, computer programming and systems administration, and opportunities within business and industry where employers need mathematicians for research and development, statistical analysis, marketing and sales.

Facilities

The Aubrey Truman Reading Room, located in the centre of the Department of Mathematics, houses the departmental library and computers for student use, and is a popular venue for students to work independently on the regular exercise sheets set by their lecturers, and to discuss mathematics together.

The main university library, the Learning and Information Centre (LIC), contains a notably extensive collection of mathematics books.

As part of our expansion, we are building the Computational Foundry on our Bay Campus for computer and mathematical sciences. This development is exciting news for Swansea Mathematics who are part of the vibrant and growing community of world-class research leaders drawn from computer and mathematical sciences.

Research

The results of the Research Excellence Framework (REF) 2014 show that our research environment (how the Mathematics Department supports research staff and students) and the impact of our research (its value to society) were both judged to be 100% world leading or internationally excellent.

All academic staff in Mathematics are active researchers and the department has a thriving research culture.



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Do you have an aptitude and passion for mathematics and statistics, a keen interest in finance and insurance and want to work for a major financial organisation in finance, insurance or the money market? This course will provide you with a deep understanding of the world of finance,and give you the ability to speak its 'language'. Read more

Do you have an aptitude and passion for mathematics and statistics, a keen interest in finance and insurance and want to work for a major financial organisation in finance, insurance or the money market? This course will provide you with a deep understanding of the world of finance,and give you the ability to speak its 'language'. This course combines theory with hands-on practical skills via an industry placement or research project –ensuring you graduate with the right skills increasingly being sought by banks and other financial institutions.

The Master of Financial Mathematics offers advanced training in the core areas of stochastic, financial and insurance modelling, statistical analysis and computational methodology, as well as in a wide range of elective topics from economics, econometrics, finance, mathematics and probability.

Graduates of this course are likely to enter specialist careers in research departments within banks, insurance and consultancy firms or derivatives of valuation and portfolio management within investment houses.

The School of Mathematical Sciences sits within the leading Faculty of Science at Monash University. This vibrant, dynamic and successful School is undergoing a period of growth with the appointment of several new senior academic staff including Professor Gregoire Loeper, Course Director for the Masters of Financial Mathematics. With mathematics as the fundamental underpinning of so many subject areas, sectors and disciplines, the School is also building ever stronger collaborations with relevant industries, including the financial sector.

Course structure

The course is structured in three Parts. Part A. Orientation studies, Part B. Specialist studies, Part C. Applied professional practice. All students complete Part B.  Depending upon prior qualifications, you may receive credit for Part A or Part C or a combination of the two.

Part A. Orientation studies

These studies provide an orientation to the field of Financial Mathematics. You will choose studies that complement your current knowledge relevant to financial mathematics, including principles of econometrics, mathematical methods and stochastic processes.

Part B. Specialist studies

These studies will provide you with advanced knowledge and skills relevant to thoughtful, innovative and evidence-based practice in financial modelling and analysis. You will acquire core knowledge of and skills in financial econometrics, and advanced mathematical modelling and computational methods in finance. You will complement these with study in areas of your choice, including interest rate modelling, Markov processes, statistical learning in finance, and global financial markets.

Part C. Applied professional practice

These studies will provide you with the opportunity to apply your knowledge skills developed in Part A and B to "real life" problems, through completing an industry project or an industry internship. Students admitted to the course who have a recognised honours degree or graduate diploma or graduate certificate in a cognate discipline including mathematics or statistics, will receive credit for this part however, should they wish to complete a 24 point research project as part of Part B they should consult with the course convenor



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Who is it for?. To successfully complete this course, you must have a very good understanding of mathematics. You may well have studied maths, physics or engineering degrees as an undergraduate. Read more

Who is it for?

To successfully complete this course, you must have a very good understanding of mathematics. You may well have studied maths, physics or engineering degrees as an undergraduate.

Or you might have a bachelor’s degree in economics or science and in particular computer science, which, coupled with your interest in stochastics, could also qualify you for this programme.

You should have a general interest in learning the more technical and mathematical techniques used in financial markets; but you don’t need to have a background in finance.

Objectives

The MSc Financial Mathematics focuses on stochastics and simulation techniques, but also covers some econometrics. You’ll study core modules covering asset pricing, risk management and an introduction to key financial securities such as equities, fixed income and derivatives.

You’ll cover a wide range of elementary and advanced topics in stochastics, including Levy processes and different simulation techniques. You’ll be taught Matlab and VBA and you have the opportunity to learn other programming languages as part of our electives offering, such as Python or C++.

There are three ways to complete the third term. Either you’ll choose five electives from around 40 optional modules in your final term. Or you can choose to complete a traditional dissertation, known as a ‘business research project’, which counts for four electives, or a shorter ‘applied research project’, which is the equivalent of two elective modules.

Structure

  • You will have gained a very good understanding of the technical aspects used in financial markets, including wide ranging financial theory and different financial assets.
  • You will gain a good understanding of stochastic and mathematical finance and gained some knowledge of econometrics and forecasting. You will also have obtained a good understanding of programming, in particular Matlab.
  • From the MSc Financial Mathematics you will also understand how the theory is being applied in the financial industry and what practical issues are.
  • In the third term you have three different options how you can complete your MSc, including a project or choosing only electives. Popular electives include Modelling and Data Analysis, Advanced Financial Engineering and Credit Derivatives, Credit Risk Management, Quantitative Risk Management. Introduction to Python.

Assessment

We review all our courses regularly to keep them up-to-date on issues of both theory and practice.

To satisfy the requirements of the degree course students must complete:

  • nine core courses (Eight at 15 credits each, one at 10 credits)

and either

  • five electives (10 credits each)
  • three electives (10 credits each) and an Applied Research Project (20 credits)
  • one elective (10 credits) and a Business Research Project (40 credits)

Assessment of modules on the MSc in Financial Mathematics, in most cases, is by means of coursework and unseen examination. Coursework may consist of standard essays, individual and group presentations, group reports, classwork, unseen tests and problem sets. Please note that any group work may include an element of peer assessment.

Two Induction Weeks

The Financial Mathematics course starts with two compulsory induction weeks, focused on:

  • an introduction to careers in finance and the opportunity to speak to representatives from over 75 companies during a number of different industry specific fairs.
  • a reminder course of advanced financial mathematics, statistics and basic computing which forms a prerequisite of the core modules in term 1.

Career pathways

The job opportunities for students from the three quants Masters programmes are very similar and students usually find employment with either large investment banks, or smaller specialist companies or financial boutique firms. Working as a quantitative analysts using stochastic, technical risk management position, pricing fixed income securities and structuring are some of the positions Financial Mathematics students are well qualified for. You will also have the skills to study for a PhD in the area of quantitative finance and financial markets.



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The University of Dundee has a long history of mathematical biology, going back to Professor Sir D'Arcy Wentworth Thompson, Chair of Natural History, 1884-1917. Read more

Mathematical Biology at Dundee

The University of Dundee has a long history of mathematical biology, going back to Professor Sir D'Arcy Wentworth Thompson, Chair of Natural History, 1884-1917. In his famous book On Growth and Form (where he applied geometric principles to morphological problems) Thompson declares:

"Cell and tissue, shell and bone, leaf and flower, are so many portions of matter, and it is in obedience to the laws of physics that their particles have been moved, molded and conformed. They are no exceptions to the rule that God always geometrizes. Their problems of form are in the first instance mathematical problems, their problems of growth are essentially physical problems, and the morphologist is, ipso facto, a student of physical science."

Current mathematical biology research in Dundee continues in the spirit of D'Arcy Thompson with the application of modern applied mathematics and computational modelling to a range of biological processes involving many different but inter-connected phenomena that occur at different spatial and temporal scales. Specific areas of application are to cancer growth and treatment, ecological models, fungal growth and biofilms. The overall common theme of all the mathematical biology research may be termed"multi-scale mathematical modelling" or, from a biological perspective, "quantitative systems biology" or"quantitative integrative biology".

The Mathematical Biology Research Group currently consists of Professor Mark Chaplain, Dr. Fordyce Davidson and Dr. Paul Macklin along with post-doctoral research assistants and PhD students. Professor Ping Lin provides expertise in the area of computational numerical analysis. The group will shortly be augmented by the arrival of a new Chair in Mathematical Biology (a joint Mathematics/Life Sciences appointment).

As a result, the students will benefit directly not only from the scientific expertise of the above internationally recognized researchers, but also through a wide-range of research activities such as journal clubs and research seminars.

Aims of the programme

1. To provide a Masters-level postgraduate education in the knowledge, skills and understanding of mathematical biology.
2. To enhance analytical and critical abilities and competence in the application of mathematical modeling techniques to problems in biomedicine.

Prramme Content

This one year course involves taking four taught modules in semester 1 (September-December), followed by a further 4 taught modules in semester 2 (January-May), and undertaking a project over the Summer (May-August).

A typical selection of taught modules would be:

Dynamical Systems
Computational Modelling
Statistics & Stochastic Models
Inverse Problems
Mathematical Oncology
Mathematical Ecology & Epidemiology
Mathematical Physiology
Personal Transferable Skills

Finally, all students will undertake a Personal Research Project under the supervision of a member of staff in the Mathematical Biology Research Group.

Methods of Teaching

The programme will involve a variety of teaching formats including lectures, tutorials, seminars, journal clubs, case studies, coursework, and an individual research project.

Taught sessions will be supported by individual reading and study.

Students will be guided to prepare their research project plan and to develop skills and competence in research including project management, critical thinking and problem solving, project reporting and presentation.

Career Prospects

The Biomedical Sciences are now recognizing the need for quantitative, predictive approaches to their traditional qualitative subject areas. Healthcare and Biotechnology are still fast-growing industries in UK, Europe and Worldwide. New start-up companies and large-scale government investment are also opening up employment prospects in emerging economies such as Singapore, China and India.

Students graduating from this programme would be very well placed to take advantage of these global opportunities.

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Overview. Scientific computing is a new and growing discipline in its own right. It is concerned with harnessing the power of modern computers to carry out calculations relevant to science and engineering. Read more

Overview

Scientific computing is a new and growing discipline in its own right. It is concerned with harnessing the power of modern computers to carry out calculations relevant to science and engineering.

By its very nature, scientific computing is a fundamentally multidisciplinary subject. The various application areas give rise to mathematical models of the phenomena being studied.

Examples range in scale from the behaviour of cells in biology, to flow and combustion processes in a jet engine, to the formation and development of galaxies. Mathematics is used to formulate and analyse numerical methods for solving the equations that come from these applications.

Implementing the methods on modern, high performance computers requires good algorithm design to produce efficient and robust computer programs. Competence in scientific computing thus requires familiarity with a range of academic disciplines. The practitioner must, of course, be familiar with the application area of interest, but it is also necessary to understand something of the mathematics and computer science involved.

Whether you are interested in fundamental science, or a technical career in business or industry, it is clear that having expertise in scientific computing would be a valuable, if not essential asset. The question is: how does one acquire such expertise?

This course is one of a suite of MScs in Scientific Computation that are genuinely multidisciplinary in nature. These courses are taught by internationally leading experts in various application areas and in the core areas of mathematics and computing science, fully reflecting the multidisciplinary nature of the subject. The courses have been carefully designed to be accessible to anyone with a good first degree in science or engineering. They are excellent preparation either for research in an area where computational techniques play a significant role, or for a career in business or industry.

Key facts:

- This course is offered in collaboration with the School of Computer Science.

- It is one of a suite of courses focusing on scientific computation.

- The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 60 full-time academic staff.

- In the latest independent Research Assessment Exercise, the school ranked 8th in the UK in terms of research power across the three subject areas within the School of Mathematical Sciences (pure mathematics, applied mathematics, statistics and operational research).

Modules

Advanced Techniques for Differential Equations

Computational Linear Algebra

Operations Research and Modelling

Programming for Scientific Computation

Scientific Computation Dissertation

Simulation for Computer Scientists

Stochastic Financial Modelling

Variational Methods

Vocational Mathematics

Data Mining Techniques and Applications

Mathematical Foundations of Programming

English language requirements for international students

IELTS: 6.0 (with no less than 5.5 in any element)

Further information



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The Master in Actuarial Science at ISEG - Lisbon School of Economics and Management, was designed according to the international requirements for the actuarial profession. Read more

The Master in Actuarial Science at ISEG - Lisbon School of Economics and Management, was designed according to the international requirements for the actuarial profession. It is meant to cover most of the course materials indispensable for the accreditation of an actuary in the European Union.

The programme offers a solid academic foundation in actuarial science, statistics and finance, providing you the skills to become a successful actuary. 

Our Masters in Actuarial Science is one of only a few to be accredited by the UK actuarial profession, the Institute and Faculty of Actuaries. A good performance can lead to exemptions from the professional examinations CT1, CT2, CT3, CT4, CT5, CT6 and CT8. If you have a BSc from ISEG you may have also exemption from CT7.

Our Master is also in the SOA (Society of Actuaries, in North America) UCAP list.

During the fourth semester, you may take a training post in an insurance company.

It is accredited by the Portuguese Agency for Assessment and Accreditation of Higher Education - A3ES.

ISEG Actuarial Science Club is entirely managed by our students. They arrange a series of seminars by industry professionals (most of them alumni from our Masters) and popular social events.

Credits: 120 ECTS

Language : English

Application: Online

Core Modules

1st Semester

Computational Tools for Actuaries

Financial Mathematics

Financial Markets and Investments

Probability and Stochastic Processes

Risk Models

2nd Semester

Generalized Linear Models

Loss Reserving

Survival Models and Life Contingencies

Risk Theory

Time Series

3rd Semester

Actuarial Topics

Asset-Liability Management

Finance and Financial Reporting

Models in Finance

Pension Funds

Ratemaking and Experience Raking

Solvency Models

4th Semester

Internship/Project/Dissertation



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About the course. This intensive introduction to advanced pure and applied mathematics draws on our strengths in algebra, geometry, topology, number theory, fluid dynamics and solar physics. Read more

About the course

This intensive introduction to advanced pure and applied mathematics draws on our strengths in algebra, geometry, topology, number theory, fluid dynamics and solar physics.

You’ll attend lectures but you’ll also get hands-on research experience, writing a dissertation supervised by an active researcher.

Your career

Our graduates go into finance, manufacturing and pharmaceuticals. They work for government agencies and research institutes with major organisations such as First Direct, GlaxoSmithKline, Marks and Spencer, the Government Statistical Service and Medical Research Council units. Our courses can also prepare you for PhD-level research.

About us

Our academics are in demand. They are members of international societies and organisations, and they speak at conferences around the world. They bring new ideas into the classroom so you can see how research is improving on existing approaches. Our solar scientists were the first to record musical sounds created by vibrations in the sun’s atmosphere.

Our Statistical Services Unit works with industry, commerce and the public sector. The services they provide include consultancy, training courses and computer software development.

Different ways to study

You can study full-time over a year or part-time over two to three years via online distance learning. The MSc Mathematics is only available as a full-time course.

Modules

Possible module choices include:

  • Algebra
  • Analysis
  • Geometry
  • Algebraic Topology
  • Number Theory
  • Topics in Advanced Fluid Dynamics
  • Analytical Dynamics and Classical Field Theory
  • Mathematical Modelling of Natural Systems
  • Stochastic Processes and Finance
  • Waves and Magnetohydrodynamics

Teaching and assessment

There are lectures and seminars. You’re assessed by exams, coursework and a dissertation.



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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.

About this degree

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 the 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.

Further information on modules and degree structure is available on the department website: Financial Mathematics MSc

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.

Recent career destinations for this degree

  • Structurer, BNP Paribas
  • PhD in Mathematics, University College London (UCL)
  • University Teacher, Chechen State University
  • CFA (Chartered Financial Analyst), Quartic Training
  • MSc Financial Mathematics, 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.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

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.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Mathematics

82% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Our MPhil/PhD degree in Mathematics and Statistics aims to train you to conduct research of a high academic standard and to make original contributions to the subject. Read more
Our MPhil/PhD degree in Mathematics and Statistics aims to train you to conduct research of a high academic standard and to make original contributions to the subject.

The programme involves coursework (where suitable) and research training, but its major component is the preparation of a substantial research thesis. The thesis should demonstrate a sound understanding of the main issues in the area and add to existing knowledge.

Research interests in mathematics and statistics include: mathematical finance, in particular the analysis of risk and numerical computation; mathematical physics and partial differential equations; approximation theory and numerical analysis; probability and stochastic processes, pure and applied; applied statistics and multivariate analysis; covariance modelling for repeated measures and longitudinal data; medical statistics; combinatorics, algebra and designs.

Our research

Birkbeck is one of the world’s leading research-intensive institutions. Our cutting-edge scholarship informs public policy, achieves scientific advances, supports the economy, promotes culture and the arts, and makes a positive difference to society.

Birkbeck’s research excellence was confirmed in the 2014 Research Excellence Framework, which placed Birkbeck 30th in the UK for research, with 73% of our research rated world-leading or internationally excellent.

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High-level training in statistics and the modelling of random processes for applications in science, business or health care. Read more

High-level training in statistics and the modelling of random processes for applications in science, business or health care.

For many complex systems in nature and society, stochastics can be used to efficiently describe the randomness present in all these systems, thereby giving the data greater explanatory and predictive power. Examples include statistical mechanics, financial markets, mobile phone networks, and operations research problems. The Master’s specialisation in Applied Stochastics will train you to become a mathematician that can help both scientists and businessmen make better decisions, conclusions and predictions. You’ll be able to bring clarity to the accumulating information overload they receive.

The members of the Applied Stochastics group have ample experience with the pure mathematical side of stochastics. This area provides powerful techniques in functional analysis, partial differential equations, geometry of metric spaces and number theory, for example. The group also often gives advice to both their academic colleagues, and organisations outside of academia. They will therefore not only be able to teach you the theoretical basis you need to solve real world stochastics problems, but also to help you develop the communications skills and professional expertise to cooperate with people from outside of mathematics.

See the website http://www.ru.nl/masters/mathematics/stochastics

Why study Applied Stochastics at Radboud University?

- This specialisation focuses both on theoretical and applied topics. It’s your choice whether you want to specialise in pure theoretical research or perform an internship in a company setting.

- Mathematicians at Radboud University are expanding their knowledge of random graphs and networks, which can be applied in the ever-growing fields of distribution systems, mobile phone networks and social networks.

- In a unique and interesting collaboration with Radboudumc, stochastics students can help researchers at the hospital with very challenging statistical questions.

- Because the Netherlands is known for its expertise in the field of stochastics, it offers a great atmosphere to study this field. And with the existence of the Mastermath programme, you can follow the best mathematics courses in the country, regardless of the university that offers them.

- Teaching takes place in a stimulating, collegial setting with small groups. This ensures that you’ll get plenty of one-on-one time with your thesis supervisor at Radboud University .

- More than 85% of our graduates find a job or a gain a PhD position within a few months of graduating.

Career prospects

Master's programme in Mathematics

Mathematicians are needed in all industries, including the banking, technology and service industries, to name a few. A Master’s in Mathematics will show prospective employers that you have perseverance, patience and an eye for detail as well as a high level of analytical and problem-solving skills.

Job positions

The skills learned during your Master’s will help you find jobs even in areas where your specialised mathematical knowledge may initially not seem very relevant. This makes your job opportunities very broad and is the reason why many graduates of a Master’s in Mathematics find work very quickly.

Possible careers for mathematicians include:

- Researcher (at research centres or within corporations)

- Teacher (at all levels from middle school to university)

- Risk model validator

- Consultant

- ICT developer / software developer

- Policy maker

- Analyst

PhD positions

Radboud University annually has a few PhD positions for graduates of a Master’s in Mathematics. A substantial part of our students attain PhD positions, not just at Radboud University, but at universities all over the world.

Our research in this field

The research of members of the Applied Stochastics Department, focuses on combinatorics, (quantum) probability and mathematical statistics. Below, a small sample of the research our members pursue.

Eric Cator’s research has two main themes, probability and statistics.

1. In probability, he works on interacting particles systems, random polymers and last passage percolation. He has also recently begun working on epidemic models on finite graphs.

2. In statistics, he works on problems arising in mathematical statistics, for example in deconvolution problems, the CAR assumption and more recently on the local minimax property of least squares estimators.

Cator also works on more applied problems, usually in collaboration with people from outside statistics, for example on case reserving for insurance companies or airplane maintenance. He has a history of changing subjects: “I like to work on any problem that takes my fancy, so this description might be outdated very quickly!”

Hans Maassen researches quantum probability or non-commutative probability, which concerns a generalisation of probability theory that is broad enough to contain quantum mechanics. He takes part in the Geometry and Quantum Theory (GQT) research cluster of connected universities in the Netherlands. In collaboration with Burkhard Kümmerer he is also developing the theory of quantum Markov chains, their asymptotic completeness and ergodic theory, with applications to quantum optics. Their focal point is shifting towards quantum information and control theory, an area which is rapidly becoming relevant to experimental physicists.

Ross Kang conducts research in probabilistic and extremal combinatorics, with emphasis on graphs (which abstractly represent networks). He works in random graph theory (the study of stochastic models of networks) and often uses the probabilistic method. This involves applying probabilistic tools to shed light on extremes of large-scale behaviour in graphs and other combinatorial structures. He has focused a lot on graph colouring, an old and popular subject made famous by the Four Colour Theorem (erstwhile Conjecture).

See the website http://www.ru.nl/masters/mathematics/stochastics



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Computational mathematics is relevant to almost every science, engineering, business and finance discipline, as well as many industrial sectors. Read more
Computational mathematics is relevant to almost every science, engineering, business and finance discipline, as well as many industrial sectors. This means it’s an excellent choice if you’re considering broadening your career options whilst studying a discipline that is in high demand in an ever technological era. Studying this MSc is also excellent preparation for academic research in any area where computational techniques play a significant role, offering you a clear route to further study at PhD level.

The course delivers an outstanding combination of advanced applicationoriented mathematical concepts and computational methodologies. Broad in scope and genuinely multidisciplinary in nature, it’s based on solid and well-founded mathematical theory.

Subject modules are carefully designed to be accessible to anyone with a good first degree in mathematics or in science and engineering subjects which have a strong mathematical component.

Here at the University of Derby, our teaching team has wide-ranging expertise in contemporary areas of mathematics and their applications to modern society. You’ll be inspired by academics and practitioners who have experience of harnessing mathematics and computing to address real-world problems.

You’ll benefit from flexible study options to fit in with your lifestyle. If you’re already in employment, you can gain this MSc through part time study while developing your knowledge and technical competence in ways that are directly relevant to your job.

You’ll study modules such as:

Optimisation
Scientific Computing
Stochastic Processes
Networks and Algorithms
Nonlinear System Dynamics
Studying at Masters Level and Research Methods
Independent Scholarship

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The financial sector is increasingly using sophisticated mathematical models to predict the development of markets and risks related to the services and products it provides. Read more
The financial sector is increasingly using sophisticated mathematical models to predict the development of markets and risks related to the services and products it provides. Banks, building societies, accountancy, insurance, investment and pension companies demand graduates with strong quantitative skills. This programme aims to prepare you for a successful career in these fields.

Taught by Mathematics at the School of Science and Engineering and Business at the School of Social Sciences, we ensure you can experience both the mathematical and economic perspective of the subject.

Why study this course at Dundee?

This courses offers a challenging range of modules across disciplines at the University of Dundee, one of the highest ranked universities in in UK for student experience and satisfaction.

Mathematics is central to many disciplines across a wide variety of fields particularly in the financial sector. You will enhance your analytical and critical abilities and competence in the application of mathematics to solve real world problems.

Demand for people with mathematical qualifications in the financial sector is particularly high and graduates of this programme will gain a significant advantage to further their career within the financial industry.

What's so good about this course at Dundee?

Mathematics at the University of Dundee consistently enjoys high rankings in student satisfaction and experience. It was ranked second in Scotland, and sixth in the UK by The Times & Sunday Times Good University Guide 2016.

The University has an international reputation for world class teaching and research. Mathematics at the University of Dundee was ranked top in Scotland and eighth in the UK for the quality and impact of its research.

You will be taught by staff who have experience applying mathematical models to financial problems or who have gained insight into the financial sector through practical experience or research.

The MSc Mathematics for the Financial Sector is a highly marketable qualification which will prepare you for a successful and rewarding career.

Teaching & Assessment

- How you will be taught

You'll learn by traditional methods such as lectures, tutorials, and workshops as well as via computer assisted learning. We also teach the use of professional mathematical/statistical/financial software packages in order to allow students to explore more complex problems and as a preparation for their future employment.

Individual reading and study takes a particularly important role in the summer project. For the project, we will guide you with preparation of your research project plan and to develop skills and competence in research including project management, critical thinking and problem solving, project reporting and presentation.

- How you will be assessed

About 50% of the assessments are written (formal) examinations, 35% are coursework/continuous assessment and the remaining 15% is the project report.

What you will study

The course comprises the following modules in Semester 1 and 2:

Compulsory

Dynamical Systems (15 credits)
Stochastic Processes (15 credits)
Global Financial Markets, (20 credits)
Optimisation in Finance and Energy (15 credits)
Derivatives & Risk Management (20 credits)

Advised

Computational Modelling (15 credits)
Econometrics for Finance, (20 credits)
Inverse Problems (15 credits)
Global Risk Analysis (20 credits)
This is complemented with a project (25 credits) over the summer, which may take the form of either a review-based analysis of applications of mathematics to the financial sector or a report based on work experience gained in the financial sector.

Non-compulsory modules of up to 30 credits can be replaced with other comparable modules such as Introduction to Data Mining and Machine Learning or Programming Languages for Data Engineering.

Employability

Employers value the precision and reasoning of mathematics graduate which makes them some of the most high-sought and highly paid graduate in the UK. This programme will equip you with analytical and decision tools required for careers in fields such as finance, banking and business.

This programme is designed distinguish our graduates from an increasingly large graduate population within these sectors.

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This MSc programme is run by Heriot-Watt University jointly with The University of Edinburgh. Upon graduation, you will receive a degree certificate bearing crests of both universities. Read more

This MSc programme is run by Heriot-Watt University jointly with The University of Edinburgh. Upon graduation, you will receive a degree certificate bearing crests of both universities.

It provides you with expertise in financial mathematics, including stochastic calculus, and a range of practical techniques for analysing financial markets. You will also learn quantitative skills for developing and managing risk that are in high demand.

Programme structure

This programme involves two taught semesters of compulsory and optional courses, followed by a dissertation project.

The teaching is shared between Heriot-Watt University (HW) and The University of Edinburgh (UoE). The timetable is arranged so that you spend three days a week at the Heriot-Watt Riccarton Campus and two days a week at University of Edinburgh’s King’s Buildings Campus.

The dissertation project is either carried out with an industrial partner (e.g. Aberdeen Asset Management, Moody’s Analytics and Lloyds Banking Group) or supervised by academics at Heriot-Watt University. The programme typically involves the following courses.

Compulsory courses:

  • Credit Risk Modelling (HW)
  • Derivatives Markets (HW)
  • Derivative Pricing and Financial Modelling (HW)
  • Discrete-Time Finance (UoE)
  • Financial Markets (HW)
  • Special Topics 1 (UoE)
  • Special Topics 2 (HW)
  • Stochastic Analysis in Finance (UoE)

Option courses:

  • Optimization Methods in Finance (UoE)
  • Financial Econometrics (HW)
  • Portfolio Theory (HW)
  • Numerical Techniques of Partial Differential Equations (HW)
  • Simulation (UoE)
  • Statistical Methods (HW)
  • Statistical Inference (HW)
  • Time Series Analysis (HW)
  • Stochastic Control and Dynamic Asset Allocation (UoE)

Work placements/internships

Adding depth to your learning, our work placement programme puts you at the heart of organisations such as Aberdeen Asset Management, Moody’s Analytics and Lloyds Banking Group.

Career opportunities

Graduates typically work in major financial institutions or continue their studies by joining PhD programmes.



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