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Masters Degrees (Financial Mathematics)

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The MSc Financial Mathematics draws on tools from applied mathematics, computer science, statistics and economic theory to prepare you for roles in which you will combine in-depth knowledge of financial products and risk with sophisticated technical and programming skills. Read more
The MSc Financial Mathematics draws on tools from applied mathematics, computer science, statistics and economic theory to prepare you for roles in which you will combine in-depth knowledge of financial products and risk with sophisticated technical and programming skills.

You will acquire solid knowledge of probability theory and stochastic processes, numerical analysis and programming languages, asset pricing theory and risk analysis, with special emphasis on valuation and risk management.

Typical career paths of graduates from our MSc Financial Mathematics include research positions (in both financial and academic institutions), or roles involving the development, management and improvement of derivatives models using advanced programming languages, and model validation such as Equity/Equity Derivatives Quant, Quantitative Financial Engineer, or Quantitative Risk Analyst.

This programme is rigorous with respect to the mathematics but also places great emphasis on linking theory with real world developments. You will often be exposed to the teaching of real world practitioners from the City of London.

Cass's proximity to the City of London, and our close links to many of its institutions, will help you to access outstanding networking and career opportunities.

Visit the website: http://www.cass.city.ac.uk/courses/masters/courses/financial-mathematics

Course detail

There are 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.

Attendance is mandatory.

Format

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

• eight core courses (15 credits each)
and
• two additional core modules plus three electives (10 credits each)
or
• three electives (10 credits each) and an Applied Research Project (20 credits)
or
• one elective (10 credits) and a Business Research Project (40 credits)

Assessment

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.

Career opportunities

Many graduates from the MSc in Financial Mathematics progress to one of two fields:

• derivatives valuation and portfolio management within investment houses
• research departments within banks and consultancy firms

Some examples of where graduates from the MSc in Financial Mathematics class of 2014 are working are:

• Bank of China - Management Trainee
• Santander - Credit Fraud Analyst
• Renaissance Re - Analyst
• Deutsche Bank - Bookrunner

How to apply

Apply here: http://www.city.ac.uk/study/postgraduate/applying-to-city

Funding

For information on funding, please follow this link: http://www.city.ac.uk/study/postgraduate/funding-and-financial-support

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The MSc Financial Mathematics programme enables graduates and professionals with a strong mathematics background to research, develop and apply quantitative and computational techniques to finance. Read more
The MSc Financial Mathematics programme enables graduates and professionals with a strong mathematics background to research, develop and apply quantitative and computational techniques to finance. It covers topics from classical options pricing to post-crisis investment and risk management. The Department of Mathematics has a superb reputation for research-led teaching and strong links to industry and our graduates are highly sought after.

Key benefits

- A rigorous approach to quantitative finance taught entirely by the Department of Mathematics.

- End-to-end coverage of the skills needed for working in the financial, actuarial or related industry: probability theory, optimisation, statistics and computer implementation.

- Unrivalled facilities, including access to live market data in our Bloomberg Data laboratory.

- A stone’s throw from the City of London's financial centre

- Full or part time study (most lectures given late afternoons)

Visit the website: http://www.kcl.ac.uk/study/postgraduate/taught-courses/financial-mathematics-msc.aspx

Course detail

- Description -

Financial mathematics plays a crucial role in all sectors of financial markets including fixed income, credit derivatives, pensions, insurances, energy and even bets on the weather. The application of mathematics has had a profound effect upon finance and has allowed the creation of entirely new markets for financial products.

The financial mathematics programme at King’s is unique in its emphasis upon mathematical rigour. It encompasses all the skills required for successful risk management, trading and research in quantitative finance: probability, statistics, optimisation, computing and financial markets. Our outstanding teaching is matched by outstanding facilities for the study and research of financial markets.

- Course purpose -

This programme is suitable for students or professionals with a strong mathematical background. It covers the principles and techniques of quantitative finance to prepare students for advanced work in the financial sector or research in mathematical finance.

- Course format and assessment -

At least eight taught modules assessed by written examinations and one individual project. Two prizes are normally awarded each year for best overall performance in the MSc in Financial Mathematics.

Career prospects

Our graduates are highly sought after by investment banks, corporate risk management units, insurance companies, fund management institutions, financial regulatory bodies, brokerage firms, and trading companies. Recent employers of our graduates include, Capital Investment, Credit Suisse, European Bank for Reconstruction & Development, Fitch Ratings, HSBC and Morgan & Stanley. Some graduates have pursued research degrees in financial mathematics.

How to apply: http://www.kcl.ac.uk/study/postgraduate/apply/taught-courses.aspx

About Postgraduate Study at King’s College London:

To study for a postgraduate degree at King’s College London is to study at the city’s most central university and at one of the top 20 universities worldwide (2015/16 QS World Rankings). Graduates will benefit from close connections with the UK’s professional, political, legal, commercial, scientific and cultural life, while the excellent reputation of our MA and MRes programmes ensures our postgraduate alumni are highly sought after by some of the world’s most prestigious employers. We provide graduates with skills that are highly valued in business, government, academia and the professions.

Scholarships & Funding:

All current PGT offer-holders and new PGT applicants are welcome to apply for the scholarships. For more information and to learn how to apply visit: http://www.kcl.ac.uk/study/pg/funding/sources

Free language tuition with the Modern Language Centre:

If you are studying for any postgraduate taught degree at King’s you can take a module from a choice of over 25 languages without any additional cost. Visit: http://www.kcl.ac.uk/mlc

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This programme provides you with advanced analytical training, quantitative knowledge and the practical skill sets required by modern financial institutions. Read more
This programme provides you with advanced analytical training, quantitative knowledge and the practical skill sets required by modern financial institutions. It aims to equip you with a solid education in financial analysis, risk management and financial engineering for a successful career in the finance and banking industries. It is heavily maths/quant weighted with advanced maths modules and programming modules designed with practical applications in mathematical/quant finance.

The programme will equip you with:
• intellectual skills and theoretical understandings appropriate for the study of financial mathematics and quantitative analysis at postgraduate level
• practical mathematics and object-oriented programming skills linking finance theories to real-world application
• skills in research, evaluation and analysis and the quantitative techniques to evaluate and interpret complex data and research literature
• skills and abilities to devise, plan and undertake complex research projects in the field of financial mathematics

Core Modules

• Advanced Modelling Methods in Finance
• Advanced Financial Econometrics
• Computational Methods in Finance
• Continuous Time Finance
• Advanced Statistics
• Stochastic Calculus
• Dissertation

Elective Modules

• Microeconomics for Financial Mathematics
• Quantitative Methods
• Numerical Computation in Finance
• Advanced Risk Management

What are my career prospects?

A graduate degree in financial mathematics gives you many employment opportunities in the business world such as in banks, investment firms, insurance companies, consulting services to financial industries, government regulators, business entities, and teaching and research at universities or research institution, and government entities.

NOTE: ICAEW ACCREDITATION IS STILL OUTSTANDING. APPLICATION IN PROGRESS.

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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.

Visit the website http://www.study.monash/courses/find-a-course/2016/financial-mathematics-s6001?domestic=true

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.

For more information visit the faculty website - http://www.study.monash/media/links/faculty-websites/science

About Mathematical Sciences

The School of Mathematical Sciences at Monash University is leading the way towards finding effective solutions to some of society's most pressing problems. Maths is the language of science and forms the basis of most of modern science and engineering. Our enthusiastic mathematicians love finding the true magic and beauty in maths and subsequently pass this passion on to their students.

Teaching

Studying maths equips you with a range of valuable, unique skills. Some of the exciting areas mathematicians at Monash are working on include mathematical modelling to predict behaviour, analysis using pure maths, and stochastic processes involving risk, randomness and change.

Mathematics and statistics are also the two cornerstones for decision making and various quantitative activities in commerce, industry, education and defence. From direct and daily experience, most companies and organisations have realised that success depends critically on the level of analytical, quantitative and statistical skills of their workforce and they therefore seek employees with a sound mathematical training.

By studying mathematics at Monash, you will also develop general skills in problem-solving, critical thinking, modelling, learning, analysis, research and creativity, which can be used wherever your career may take you.

Research

The School of Mathematical Sciences focuses on these main areas of research:

- Applied and Computational Mathematics
- Pure Mathematics
- Stochastic Processes

Find out how to apply here - http://www.study.monash/courses/find-a-course/2016/financial-mathematics-s6001?domestic=true#making-the-application

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A combination of mathematics, statistics and computing, financial mathematics is a specialism vital to the day-to-day functioning of the world's economic institutions. Read more
A combination of mathematics, statistics and computing, financial mathematics is a specialism vital to the day-to-day functioning of the world's economic institutions. Highly technical and theoretical aspects of mathematics take on a practical importance which can affect millions of lives through this fascinating discipline, which involves predicting the behaviour of markets and suggesting strategies for investment.


Why study MSc Financial Mathematics at Middlesex?

We believe strongly that the work you do must be relevant to the world of work – that's why our course has a strong practical slant. It also has the unusual and significant advantage of including from-scratch training in computer programming, allowing you to develop first-class computing skills alongside your mathematical expertise.You'll need a good degree in maths, or a related subject like physics or engineering, but no prior knowledge of finance.

Course highlights

Our course combines a comprehensive grounding in the theory of financial mathematics with thorough practical training
Our subscriptions to Bloomberg and Datastream allow you to work with real datasets
We'll teach you to code in widely-used languages such as C++, Java and Python, without the need for prior experience
Guest lectures from industry specialists allow you to gain insights from practising professionals into real-life situations
The course is designed either for graduates considering a financial career, or for those already working in the industry looking for a greater understanding of finance and insurance risk

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The Master of Science in Financial Mathematics Program prepares students for jobs in the financial sectors, including investment banks, hedge funds, asset management firms and security companies that require substantial quantitative capabilities to solve practical problems in pricing derivatives, risk management, assets and liabilities management, and trading strategies. Read more
The Master of Science in Financial Mathematics Program prepares students for jobs in the financial sectors, including investment banks, hedge funds, asset management firms and security companies that require substantial quantitative capabilities to solve practical problems in pricing derivatives, risk management, assets and liabilities management, and trading strategies. These jobs include quantitative analysts, derivatives traders, quantitative programmer, risk managers, sales of structured products and statistical analysts.

The curriculum is designed as a one-and-half year study for full-time students and three-year study for part-time students. Our Program is known for its solid curriculum that embraces option pricing theory, portfolio theory, risk models, time series analysis of financial data, financial economics, computer programming. We do expect students to be capable and efficient learners; and specifically, they are required to have a solid background in undergraduate level mathematics, statistics and computing.

We continue to enhance our curriculum through the introduction of new courses based on market relevance and recent innovative developments in the field of quantitative finance. In recent years, new courses on algorithm trading, market microstructure, financial computing, and structuring and trading strategies have been offered. The academic courses are offered by faculty members in the Mathematics Department that are actively engaged in various research areas in financial mathematics and stochastic analysis. Some of these faculty members have related consultancy experiences on industrial finance projects and used to deliver industrial courses to finance practitioners. The Program also invites a number of seasoned practitioners to teach market related courses. These industrial instructors are high level finance professionals who have been working in top international financial institutions (like Goldman Sachs, JP Morgan, BNP, HSBC, Fitch, etc.) and hold PhD degrees in science / engineering from leading universities (like Harvard, Cambridge, Oxford, University of Michigan, UCLA, etc.).

Programme Objectives

The Master of Science (MSc) program in Financial Mathematics aims to prepare students from quantitative disciplines for security pricing, trading strategies and risk management. The curriculum includes mathematical, statistical and computational methods for security pricing, asset allocation, speculative trading, and risk management, and offers comprehensive coverage on financial markets and valuable insights on the performance of various pricing models in market practice.

Graduates from this program are well prepared for jobs in trading and market making of derivatives, financial product development (structured products, insurance products etc.), investment decision making (fund management, trading strategies, etc.), and risk management (risk assessment, stress testing, etc.).

On completion of the program, students are expected to have:
-Comprehensive knowledge of financial products commonly traded in the markets and solid understanding of models of security pricing and hedging in equity, fixed-income, forex and credit markets.
-Solid understanding of the principles and technologies for risk management and trading strategies.
-The ability to construct quantitative models and use them for production through quantitative programming.

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The M.Sc. in Financial Mathematics and Computation is designed to provide a mature understanding of financial mathematics and computational methods. Read more
The M.Sc. in Financial Mathematics and Computation is designed to provide a mature understanding of financial mathematics and computational methods. The focus of the course is on computational techniques for finance, on mathematical modelling and on mathematical and economical theories of finance. The course is mainly a result of the collaboration between the Mathematics and Economics Departments, both very strong in research and teaching, with a valuable input from the Computer Science Department and Management Centre. See further deatails at http://www.math.le.ac.uk/CFMMSC/cfmmsc.html

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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. Read more

About the MSc programme

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 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 programme aims to develop your understanding of quantitative methodologies and techniques which 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.

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

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

Core modules

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

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

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


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

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

Elective modules

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

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


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


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

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

Research project

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

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The Certificate in Financial Mathematics (CT1) is one of nine Core Technical subjects offered by the Institute and Faculty of Actuaries, providing a basic grounding in financial mathematics and its simple applications. Read more
The Certificate in Financial Mathematics (CT1) is one of nine Core Technical subjects offered by the Institute and Faculty of Actuaries, providing a basic grounding in financial mathematics and its simple applications

This course is taught by distance learning, using materials and technology already used on our MSc/PGCert in Actuarial Science. It can be taken as a stand-alone course or as a ‘taster’ for the degree. The course tuition will prepare you for the IFoA exam which can be sat in April or September.

You will receive:
•Access to the e-book of the industry-standard text for CT1, 'An Introduction to Mathematics of Finance: A Deterministic Approach' by Professor Stephen Garrett.
•This covers all material with worked examples
•Access to the University's virtual learning environment, Blackboard
•Electronic forums and podcasts used to support your learning
•Email access to Professor Stephen Garrett who will respond to your questions within 48 hours
•Regular assignments with full marking and feedback
•A mock examination with full marking and feedback
•Access to the University's extensive digital library

You may also attend an intensive one-day tutorial held shortly before the exam, on the University's main campus, for which there will be an additional cost of £120

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Programme structure. The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. Read more
Programme structure
The programme offers four "core" modules, taken by all students, along with a variety of elective modules from which students can pick and choose. There are examinations and coursework in eight modules altogether, including the four core modules. Additionally, all students complete a dissertation.

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


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


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


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


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

Elective modules

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


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

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

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


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

Research project

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

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This masters is run jointly with Heriot-Watt University. It provides you with expertise in financial mathematics, including stochastic calculus, and a range of practical techniques for analysing financial markets. Read more

Programme description

This masters is run jointly with Heriot-Watt University. 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 since the recent financial crisis.

Adding depth to your learning, our work placement programme puts you at the heart of organisations such as Aberdeen Asset Management, Barrie & Hibbert and Lloyds Banking Group.

Programme structure

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

Compulsory courses:

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

Option courses:

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

Career opportunities

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

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This programme focuses on the mathematical and statistical methods relevant to financial markets, and the skills and knowledge needed to apply them in practice. Read more
This programme focuses on the mathematical and statistical methods relevant to financial markets, and the skills and knowledge needed to apply them in practice.

You will gain knowledge of advanced finance concepts while developing quantitative, mathematical and research skills. The programme covers key topics including financial derivative pricing, discrete and continuous time models, risk management and portfolio optimisation, as well as statistical methods for finance.

This Masters is ideal if you have previously studied accounting and finance, economics, mathematics, physics or computing, and are interested in gaining an understanding of how your skills can be applied to financial markets.

It is also an excellent qualification if you are seeking to pursue further postgraduate study, and is recognised by the Engineering and Physical Sciences Research Council (EPSRC).

Your Career

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

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 UKGovernment Actuary’s Department.

We help you to achieve your career ambitions by providing professional development support as part of the Masters programme.

Apply now

The deadline for International applications is 31 March 2017.
The deadline for UK/EU applications is 31 August 2017.
For further guidance on how to apply, visit: http://business.leeds.ac.uk/masters/how-to-apply/

Online events

You can logon from anywhere to join one of these one hour events, which will include:

- Introduction to Leeds University Business School and Masters study
- Advice from one of our Professional Development Tutors, with examples of the career skills you can build and the major companies we work with
- Guidance on how to apply for postgraduate study from our admissions team
- Opportunity to ask your questions

The dates for our next online events are below:

- Thursday 15 December 2016
- Wednesday 22 February 2017

Open events

Our Masters Open Days are an opportunity to visit in person, learn more about our Masters degrees and meet our staff. Activities include:

- Presentations, talks and Q&A with academic directors
- Business School tour
- Speak to admissions, academic teaching teams and current Masters students

Our next Masters Open Days:

- Thursday 6 April 2017
- Wednesday 21 June 2017

Please visit our website for further information and to book your place: http://business.leeds.ac.uk/masters/meet-us-at-an-event/

Read less
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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This masters programme covers the advanced mathematics that has revolutionised finance since the works of Black, Scholes and Merton in the early seventies. Read more
This masters programme covers the advanced mathematics that has revolutionised finance since the works of Black, Scholes and Merton in the early seventies. This programme is aimed at those students who are passionate about mathematics and driven to make a career amongst the many and varied financial institutions throughout the world.

The programme, which is part of the Maxwell Institute for Mathematical Sciences, the joint research institute of mathematical sciences at the University of Edinburgh and Heriot-Watt University, provides an intensive training in the mathematical ideas and tools vital to the finance industry. By developing essential new mathematical concepts, especially in stochastic calculus, and placing the mathematics in the contexts of financial markets, derivative pricing and credit risk, the programme equips students for a range of exciting and potentially lucrative career opportunities.

The programme is delivered jointly between Heriot-Watt University and the University of Edinburgh. This means you will be enrolled as a student at both univerities and benefit from access to all the services and facilities each university has to offer.

Teaching is delivered by renowned academics from both Heriot-Watt and the University of Edinburgh - some classes will therefore take place at Heriot-Watt's campus and others at the University of Edinburgh campus. Successful students will graduate with a degree awarded jointly by Heriot-Watt and the University of Edinburgh and both names will appear on the graduation certificate.

Programme content

Core courses

Derivatives Markets
Derivative Pricing and Financial Modelling
Financial Markets
Discrete-Time Finance
Stochastic Analysis in finance
Credit Risk Modelling
Special Topics, including industry lead projects

Options

Statistical Methods
Financial Econometrics
Time Series Analysis
Modern Portfolio Theory
Optimisation Methods in Finance
Numerical Methods for PDEs
Simulation in Finance
Deterministic Optimisation Methods in Finance

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