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MSc in Financial Modelling and Optimization

Programme Information

2012-2013

MSc in FMO, School of Mathematics, The University of Edinburgh 2

Contents 1 Introduction .....................................................................................................................................................4

2 Programme aims ............................................................................................................................................4

3 Administration ................................................................................................................................................5

3.1 Programme Director ............................................................................................................................................... 5

3.2 Programme Secretary ............................................................................................................................................. 5

3.3 Directors of Studies ................................................................................................................................................. 5

3.4 Representation and feedback .............................................................................................................................. 6

4 Teaching and learning approach ..............................................................................................................6

4.1 Core courses ............................................................................................................................................................... 6

4.2 Optional courses ....................................................................................................................................................... 6

4.3 Dissertation ................................................................................................................................................................. 7

4.4 Attendance................................................................................................................................................................... 8

4.5 Combining work and study .................................................................................................................................. 8

4.6 Coursework, cover sheets and group assignments .................................................................................... 8

4.7 Plagiarism .................................................................................................................................................................... 8

4.8 Electronic submission and self-checking for plagiarism ........................................................................ 10

5 Dissertation ................................................................................................................................................... 10

5.1 Role of the academic supervisor ...................................................................................................................... 11

5.2 Assessment criteria ............................................................................................................................................... 11

5.3 Dissertation format ................................................................................................................................................ 12

6 Programme Structure ................................................................................................................................ 12

6.1 Programme dates ................................................................................................................................................... 12

6.2 Examinations ............................................................................................................................................................ 13

6.2.1 Dictionaries in Examinations .................................................................................................................... 13

6.2.2 Calculators ........................................................................................................................................................ 13

7 Assessment requirements ....................................................................................................................... 13

7.1 Unsatisfactory performance............................................................................................................................... 14

7.2 Appeals ........................................................................................................................................................................ 14

8 Individual course details .......................................................................................................................... 15

8.1 Core courses ............................................................................................................................................................. 15

8.2 Research-Linked Topics ....................................................................................................................................... 20

8.3 Optional courses ..................................................................................................................................................... 20

9 Facilities ......................................................................................................................................................... 28

9.1 James Clerk Maxwell Building ........................................................................................................................... 28

9.2 University Library .................................................................................................................................................. 28


9.3 MSc Workroom ........................................................................................................................................................ 28

9.4 Careers service ........................................................................................................................................................ 28

9.5 Computer facilities ................................................................................................................................................. 29

9.6 Travel ........................................................................................................................................................................... 29

Appendix I – Institute for Academic Development Flyer ................................................................... 30


1 Introduction

This handbook is a guide to what is expected of you on this MSc in Financial Modelling and

Optimization programme, and the academic and pastoral support available to you. Please read it

carefully. It will help you to make the most of your time on the Programme. Throughout this

document references to the ``MSc in Financial Modelling and Optimization programme'', ``FMO

MSc'', or simply the ``Programme'' refer to the degree programme in Financial Modelling and

Optimization unless stated otherwise.

Some important general aspects covered in this handbook are amplified in the University of

Edinburgh Code of Practice for Taught Postgraduate Programmes, which is available from

http://www.docs.sasg.ed.ac.uk/AcademicServices/Codes/CoPTaughtPGProgrammes.pdf

and which you are also expected to read. This handbook does not supersede the University of

Edinburgh Regulations, which are available from

http://www.ed.ac.uk/schools-departments/academic-services/policies-regulations

Disclaimer: Every effort has been made to ensure the contents of this booklet are accurate at the

time of printing. Unforeseen circumstances, however, may necessitate changes to the procedures,

curricula and syllabuses described. The School undertakes to operate within the rules and

regulations as set out in the University Calendar and the Assessment Regulations. It will also

honour undertakings made in writing to individual classes, insofar as these do not conflict with

the University's regulations.

Large print: Large print version of this Guide and other documents issued to students by the

School can be made available. Students requiring these should contact the Mathematical Teaching

Organisation (MTO). (Telephone 0131 650 6427)

2 Programme aims

The aims of the MSc in Financial Modelling and Optimization are:

provide a flexible syllabus of study relevant to the needs of employers today in areas such

as the financial sector, energy markets and those that use modern financial tools and

optimization techniques;

facilitate the professional development of students (with a strong mathematical

background) in the theory and practice of financial mathematics and optimization and lay

the foundations for a successful career to the benefit of the economy and society;

provide a sound knowledge base in the fields studied and develop the wider process skills

of Problem Solving (through the application of advanced mathematical techniques from

the areas of Modern Probability Theory, Stochastic Analysis and Optimization), Team

Working and Time/Task Management;

http://www.docs.sasg.ed.ac.uk/AcademicServices/Codes/CoPTaughtPGProgrammes.pdf

http://www.ed.ac.uk/schools-departments/academic-services/policies-regulations


3 Administration

3.1 Programme Director

Dr Sotirios Sabanis

School of Mathematics

Room 4610

James Clerk Maxwell Building

The King’s Buildings

The University of Edinburgh

Edinburgh EH3 9BW

Tel: +44 (0)131 650 5084

Email: [email protected]

The Programme Director is responsible for the smooth running of the programme, including

promotion and admission, plus coordination of teaching delivery, examinations, programme

evaluation, and curriculum development. It is also the Programme Director's role to act as

arbitrator in the case of requests for deadline extensions and any other academic issues relating to

individual courses that cannot be resolved between the student and the course lecturer.

3.2 Programme Secretary

Mrs Katy McPhail,

School of Mathematics

Room 5211

James Clerk Maxwell Building

The King’s Buildings

The University of Edinburgh

Edinburgh EH3 9BW

Tel: +44 (0)131 650 4885

Email: [email protected]

3.3 Directors of Studies

Each student will be assigned to a Director of Studies. Your Director of Studies is available as a

first line of advice for any academic issues which may arise whilst you are on the Programme.

He/she is charged with facilitating your orientation and smooth progression through the degree,

from initial induction to subsequent course choice, and the transition into the project/dissertation

stage to successful completion. He/she is also available to provide first line pastoral support. You

are strongly advised to inform your Director of Studies immediately of any problems that are

interfering with your coursework or progress through the Programme, including any religious or

medical requirements that might affect your participation in any aspect of the Programme. Other

sources of specialist academic and pastoral support are listed in Appendix IV of the Code of

Practice for Taught Postgraduate Programmes.

mailto:[email protected]



3.4 Representation and feedback

Student feedback and evaluation is a valued input to curriculum and programme review and

development of the University of Edinburgh. Formally, students are asked to complete evaluation

forms for each course they take, and to attend (or select representatives for) staff-student

meetings. Representatives are also welcome to participate in the Edinburgh University Students'

Association. Informal feedback is welcome at any time.

4 Teaching and learning approach

The study programme for the FMO MSc consists of:

9 compulsory courses,

optional courses,

1 project.

4.1 Core courses

The core courses deal with the technical knowledge and practical skills that are essential for

anyone who is to graduate with an MSc in Financial Modelling and Optimization. There are nine

core courses:

Discrete-Time Finance (15 points S1)

Stochastic Analysis in Finance I (7.5 points S1)

Fundamentals of Optimization (10 points S1)

Finance, Risk and Uncertainty (10 points, S1)

Research-Linked Topics (5 points S1-2)

Stochastic Analysis in Finance II (7.5 points S2)

Risk-Neutral Asset Pricing (15 points S1 & 2)

Simulation (10 points S2)

Optimization Methods in Finance (15 points S2)

Full descriptions of these courses can be found in Section 8.

4.2 Optional courses

The optional courses allow each student to specialise in a range of skills that suits his/her own

career development. By 26 October you must have made a provisional choice of optional courses

so that you are registered for a full 120 points for the taught component. You may modify this

choice at a later date, particularly in respect of optional courses running in Semester 2. By 22

February you will be required to make a final choice of the optional courses in which you wish to

be assessed. Your Director of Studies will discuss with you the appropriate choice of optional

courses based on your background and progress on the programme to date.

The list of optional courses below is provisional at this stage. Optional courses will not normally

run with fewer than 5 students.

o Introduction to Java Programming (10 points S1)


o Parallel Numerical Algorithms (10 points S1)

o Nonlinear Optimization (10 points S2)

o Dynamic and Integer Programming (10 points S1)

o Game Theory (5 points S1)

o Credit Scoring and Data Mining (10 points S2)

o Financial Risk Management (10 points S2)

o Risk Analysis (5 points S2)

o Stochastic Modelling (10 points S2)

o Combinatorial Optimization (5 points S2)

o Large Scale Optimization (10 points S2)

o Stochastic Optimization (5 points S2)

o International Money and Finance (10 points S2)

o Advanced Time Series Econometrics (10 points S2)

Full descriptions of these courses can be found in Section 8.

4.3 Dissertation

During the period from June to August, candidates for the MSc work on a project on an approved

topic and write a dissertation based on this work. The project gives the student the opportunity to

apply skills developed earlier in the programme to real problems in Financial Mathematics and

Optimization. Projects often take the form of a consultancy exercise for a sponsoring organisation.

Students are strongly encouraged to seek the opportunity to do their project in collaboration with

an outside partner in a bank, financial institution or an organisation which has a requirement for

consultation by a Financial Mathematics analyst with specialisation in Optimization. A wide

variety of organisations provide project topics and assist with their supervision. Projects need to

be accepted by the Project Coordinator, Dr Miklos Rasonyi to ensure they are suitable for an MSc.

Here is a list of different types of dissertations. This list is not exhaustive, nor are its members

mutually exclusive; it is just meant to give some ideas about what makes an acceptable

dissertation.

A subject review surveys a chosen area, summarising the research literature and providing

an overview of its development, importance, methodology and outstanding problems.

A theoretical essay describes, in considerable depth, some piece of mathematical theory

relevant to finance. Papers in research journals are often very terse and assume a lot of

prior knowledge on the part of the reader; and acceptable project could be to explain a

recent paper, making its results more accessible and putting them in context.

A numerical project would describe and implement one or more numerical methods for

pricing, hedging or reserving for derivatives or portfolios, and perhaps aim to measure

how well it performed using real or simulated data.

A data-based project would analyse market or other data, fitting them to suitable models

and drawing conclusions.


4.4 Attendance

Lecture attendance is compulsory. The Programme Director must be notified in writing, or by e-

mail, of absence of more than a week from lectures for medical, personal, or other reasons.

4.5 Combining work and study

It should be stressed that the programme is full-time. You should expect to spend 40 hours per

week attending classes, working on the delivered material and preparing assignments for

submission. Unless you manage your time well, there will be weeks (particularly towards the end

of Semester 2) when you will have to work significantly more than 40 hours. As a consequence, it

is recommended that students do not take any employment. The University recommends a full

time student not to work more than 16 hours per week during term or during the summer months

when the MSc dissertation is prepared. Students can take a part-time job only under the condition

that such an activity will not adversely affect their performance on the MSc. Any part-time job

which would exceed 16 hours per week needs special permission from the Programme Director.

4.6 Coursework, cover sheets and group assignments

The coursework requirements---case studies, essays, and other projects---vary between courses,

as does the balance of the methods of assessment. The weighting of coursework and examinations

for individual courses is given in the information for each course.

All coursework must be submitted with a completed cover sheet, stapled in the top left corner, and

handed to the Programme Secretary, Mrs Katy McPhail, JCMB Room 5211. Completed work must

not be handed directly to any other member of staff or submitted by any other means. Cover

sheets are available in JCMB 5211 (and on-line) and have a number of functions.

They provide fields for a clear statement of the student's name and matriculation number

They contain an `òwn work declaration'' that may be used in cases of suspected

plagiarism

They allow comments on the coursework to be communicated to the lecturer

They enable the coursework mark and written feedback to be returned to the student

Until a completed cover sheet has been provided, the work will not be considered to have been

submitted.

All students must adhere to deadlines for the submission of work. Work handed in late will

incur a penalty in that the mark will be reduced by 5% of the maximum obtainable mark per

working day up to five days, after which a mark of zero will be given. Note that the reference to

``days'' includes weekends and public holidays. Students may not, for example, submit work on a

Monday morning for a Friday deadline in the expectation that no late penalty will be applied. If

there is likely to be a delay due to illness or other crisis, the Programme Director must be

informed in writing so that an extension may be considered.

4.7 Plagiarism

The following is based on an extract from the guidelines for Colleges on the avoidance of

plagiarism:


The University's degrees and other academic awards are given in recognition of the

candidate's personal achievement. Plagiarism (that is to say the action of including or copying,

without adequate acknowledgement, the work of another in one's own work) is academically

fraudulent, and an occurrence against University discipline.

Plagiarism, at whatever stage of the candidate's course, whether discovered before or after

graduation, will be investigated and dealt with appropriately by the University. If after

investigation it is established that work submitted has been plagiarised to a significant extent,

that will be permanently noted on a candidate's record.

Cheating and plagiarism are academic occurrences. Plagiarism can be defined as the act of

including or copying, without adequate acknowledgement, the work of another in one's own work

as if it were one's own. The University's procedures used in case of a presumed plagiarism can be

found in the University's Code of Practice for Taught Postgraduate Programmes and the University

Regulations.

The University's full information on plagiarism

http://www.ed.ac.uk/schools-departments/academic-services/students/postgraduate-

taught/discipline/plagiarism

includes specific guidance for undergraduate/postgraduate taught students

http://www.docs.sasg.ed.ac.uk/AcademicServices/Discipline/StudentGuidanceUGPGT.pdf

This includes the University's procedures for dealing with different kinds of plagiarism and advice

about what to do if you are accused of plagiarism. If you are still unsure how to avoid plagiarism,

having read these guidance notes, then you should approach the Programme Director for further

advice.

The University of Edinburgh encourages discussion in the preparation of all work and cooperation

in finding sources of material. This is essential in group work, but any work submitted in an

individual's name must be prepared solely by that person. Using any published source

whether private, public or from the internet without providing a full reference, or using other

students' work are disciplinary occurrences which could lead to reduced or no marks being

awarded for the work, and in extreme cases to immediate termination of studies. Students should

therefore make sure that they are quite clear about the status of every piece of work they submit,

including computer based material such as spreadsheets and computer programs. An individual

assignment must be wholly and exclusively the work of the student submitting the

assignment. Any common material found in such assignments may be treated as plagiarism, with

serious consequences for the students concerned.

The definition of plagiarism varies from educational system to educational system, and this can be

a source of misunderstandings. However, it must be absolutely clear that students must not copy

another person's work or claim another person's work as their own. Students must be aware of

possible dangers in this area. For this reason:

Always ensure the references for quotations are provided.

Do not take an extract from any text without placing quotation marks around it and

referencing the source.

If a diagram is used or adapted, give a reference to the source.

http://www.ed.ac.uk/schools-departments/academic-services/students/postgraduate-taught/discipline/plagiarism

http://www.ed.ac.uk/schools-departments/academic-services/students/postgraduate-taught/discipline/plagiarism

http://www.docs.sasg.ed.ac.uk/AcademicServices/Discipline/StudentGuidanceUGPGT.pdf


If, in an assignment, a student draws upon work performed by him/her-self outside the

programme or prior to attendance on the programme, this must be clearly indicated in the

assignment.

If in doubt, check with the Programme Director.

Past experience shows that specific procedures are required to prevent, deter and detect

plagiarism. To prevent plagiarism of certain important handwritten assignments in core courses,

they will be written up under supervision. To detect plagiarism in typeset work, all the

submissions will analysed using the plagiarism detection software “Turnitin". It is hoped that

these procedures, together with the sanctions that can be applied if plagiarism is detected, will

deter the members of the FMO MSc class who might, otherwise, cheat in order to get higher marks.

4.8 Electronic submission and self-checking for plagiarism

In addition to the hard copy, most assignments requiring typeset work (and the dissertation

project) must be submitted electronically. This is done via “Learn”, from “MyEd". Independently,

students will also be able to perform a self-check for plagiarism using “Turnitin". This is also

available from “MyEd". This will compare the text of your submission against the following

sources

Turnitin's student paper repository

Current and archived internet

Periodicals, journals and publications

What you self-check for plagiarism will not be compared with submissions from other students on

the Programme, nor will it be retained in Turnitin's student paper repository. The final, formal

submission that you make via WebCT will be checked for plagiarism against the sources above and

submissions from other students on the Programme. It will also be retained in Turnitin's student

paper repository.

5 Dissertation

The dissertation gives students the opportunity to make use of the knowledge and skills

developed on the programme, frequently by working on a real mathematical finance problem

within an organisation, although dissertation projects may also be desk/library based.

Prior to the final assessment of the taught component of the MSc programme, all students are

considered as MSc candidates. Following the Board of Examiners meeting in June, students who

complete the taught component at MSc level proceed to the dissertation stage of the MSc

programme. The award of the MSc degree thereafter depends solely on the achievement of a

dissertation mark of at least 50%.

It is the responsibility of each MSc candidate to prepare a dissertation on a subject chosen by

agreement with a member of staff who will act as an Academic Supervisor. Dissertation topics will

be agreed by mid-May. Detailed work will be carried out during the months of June, July and

August, with sufficient time being allocated to writing up the dissertation. In many cases the

research for the dissertation will involve working with an outside organisation for at least part of

the summer months.


University regulations require full-time postgraduate students to be in Edinburgh for the duration

of the Programme, unless specifically granted a leave of absence. This will not be given to enable

the student to submit a dissertation early in order to return home prior to the end of the

programme. Completing a dissertation in less than the time available is also extremely unwise as

early completion may adversely affect the standard of work and presentation.

Two typeset copies of the dissertation must be submitted to the Programme Secretary (JCMB

Room 5211) by 2.00pm on 16 August 2013. If commercial confidentiality requires that a

dissertation be treated as confidential, this can be arranged by informing the office at the time of

submission. Confidential dissertations will be read by the Academic Supervisor and examiners,

and will not be available for reference.

Dissertations are read by two internal examiners before being reviewed by the External Examiner.

A copy of the dissertation can be collected by the student after the final Board of Examiners

meeting in September.

You are strongly advised to keep a back-up draft of your dissertation and not to use a USB

pendrive for this purpose since they are easily lost or damaged. No compensation or extension

will be given for work or data lost by students.

5.1 Role of the academic supervisor

The Academic Supervisor will give advice on the subject area, relevant literature,

presentation format, methodology, structure of the dissertation, and scheduling of the

work to be done. The final responsibility for the dissertation always lies with the student.

Advisers are not expected to read and amend chapters, but they may require periodic

progress reports and sample chapters. The responsibility for the quality and content of a

dissertation lies with the author of the dissertation.

Academic staff acting as Academic Supervisors cannot be expected to be available at all

times, especially during the summer period, although staff will provide back-up facilities

during their absence. Meetings should be arranged between Academic Supervisors and

students at regular intervals, as appropriate. These meetings are primarily the initiative of

the student. The frequency of contact with Academic Supervisors depends on the wishes of

the individual student and Academic Supervisor, but students should try to discuss

progress with their Academic Supervisors at least once every 2 or 3 weeks, with more

frequent discussions in the early stages.

In the case of projects based in an outside organisation, Academic Supervisors may visit

the students in the organisation.

Students may ask their Academic Supervisors to read a draft of part of the dissertation, but

it is up to the Academic Supervisor's professional judgement as to how much of the

dissertation he or she is willing to read. Clearly, an Academic Supervisor cannot examine a

dissertation before it is formally submitted and any comments which an Academic

Supervisor makes on a draft are provisional in that the Board of Examiners may come to a

decision which differs from that of the Academic Supervisor.

5.2 Assessment criteria

All dissertations are expected to conform to the following standards:


The dissertation must add to the understanding of the dissertation subject.

The dissertation must show awareness of the relevant literature.

The dissertation must contain relevant analysis: an informed description of a problem is

not sufficient.

The dissertation must be presented using a satisfactory standard of English.

Students should inform their Academic Supervisor and the Programme Director of any factors that

will adversely affect their ability to work on their dissertation topic. Extenuating circumstances

will be taken into account by the Board of Examiners, but this information must be available prior

to the meeting of the Board. Exceptionally, it is possible for extensions to be granted if justified by

illness or other personal problems. This can be done if relevant information is given to the

Academic Supervisor or the Programme Director.

5.3 Dissertation format

Dissertations should consist of the following:

- Title page

- Abstract

- Acknowledgements

- Own work declaration

- Table of contents

- Main text (including introductory chapter and final chapter on conclusions and/or

recommendations)

- Appendices (optional)

- Bibliography

The main text of the dissertation must not exceed 35 pages, based upon a 12-point font size

and 1.0-line spacing. The main text referred to here does not include such things as tables, graphs,

figures, appendices and computer code.

Dissertations must be type set on one side of white A4 paper only. The following minimum

margins must be observed.

Left 30mm; Right 15mm; Top 15mm; Bottom 20mm

The pages in the main text, appendices and bibliography must be numbered consecutively.

6 Programme Structure

6.1 Programme dates

Induction Week: 10-14 September

Semester 1

o Teaching period: 17 September - 30 November

o Revision week: 3 December- 7 December

o December exams: 10 December - 21 December

o Winter break: 24 December – 11 January


o The School of Mathematics will be closed during the period 24 December - 3

January (inclusive) and (for security reasons) students will not be able to gain

admittance.

Semester 2

o Teaching period: 14 January - 5 April

o Spring break: 8 April - 19 April

o Revision week: 22 April - 26 April

o May exams: 29 April - 24 May

o Summer break: 27 May - 31 May

MSc Project (dissertation): 3 June - 16 August

MSc dissertations are to be submitted by 2.00pm on 16 August 2013. Late submissions

are penalised: the mark will be reduced by 5% of the maximum obtainable mark per day

for up to five days, after which a mark of zero will be given.

6.2 Examinations

Students must attend all examinations. Students who do not attend an examination will be

deemed to have failed it unless an appropriate medical certificate is sent to the Programme

Director within five days of the date of the examination. Students will not be excused from the

examinations because of holiday plans.

The Registry will give details of the location of each examination once this is known. Information

on the form of the examination will be given for each course.

There are no resit examinations for any of the courses on the programme.

6.2.1 Dictionaries in Examinations

No student is permitted to take any dictionary into an examination without written permission.

Please consult the programme secretary Mrs Katy McPhail.

6.2.2 Calculators

Only a calculator from the following list (specified by the College of Science and Engineering) may

be used in examinations.

Make Model

Casio fx85 (any version, e.g. fx85WA, fx85MS)

Casio fx83 (any version)

Casio fx82 (any version)

7 Assessment requirements

To determine the overall assessment of the taught component on the University Common Marking

Scale (UCMS), courses are weighted. Each core course worth 15 points has a weighting of 1/8 (or

15/120), each core course worth 10 points has a weighting of 1/12 (or 10/120) and each core


course worth 7.5 points has a weighting of 1/16 (or 7.5/120). The total weight of core courses is

3/4.

Similarly, each optional course worth 15 points has a weighting of 1/8 each optional course worth

10 points has a weighting of 1/12 and each optional course worth 7.5 points has a weighting of

1/16. Students must choose optional courses having a total weight of 1/4.

The UCMS mark for the taught component of the programme is the weighted average over courses

(core and optional courses). Sufficient conditions for the various awards that can be made are set

out in Table 1.

Upon completion of the taught component of the programme, any student satisfying the

conditions set out in the penultimate column of Table 1 will be permitted to proceed to the

dissertation. Note that candidates are not allowed to re-sit a paper in order to be considered for

the award of MSc.

Diploma Dip With Distinction

MSc MSc with Distinction

All courses average 40% 70% 50% 70% 80 points at 50% No No Yes Yes 80 points at 40% Yes Yes Yes Yes Project/Dissertation No No 50% 70%

Table 1: Sufficient conditions for a given award. Percentages are UCMS

7.1 Unsatisfactory performance

Under the rules drawn up by the University, the Head of the School of Mathematics can, on the

advice of the Programme Director, formally request a student who is not performing adequately,

or has otherwise breached University discipline, to withdraw from the programme at any time

during the programme.

7.2 Appeals

The University regulations for postgraduate appeals can be found in the University's Code of

Practice for Taught Postgraduate Programmes and the University Regulations.


8 Individual course details

8.1 Core courses

All core courses are compulsory.

8.1 Core courses

Discrete-Time Finance (DTF, 15 points S1)

Lecturer : Sotirios Sabanis

Delivery: Steady

Aims: To introduce, in a discrete time setting, the basic probabilistic ideas and results needed for

the later stochastic process and derivative pricing courses. By the end of the course students will

be expected to understand discrete martingale theory and its relationship with financial concepts

such as arbitrage.

Syllabus:

I. Theory

Introduction to background probability theory.

Conditional expectation.

Discrete parameter martingales, sub- and supermartingales, martingale convergence and

inequalities.

Stopping Times, Optional Stopping Theorem, Snell Envelopes.

Stopping times and Doob's Optional Stopping Theorem.

Central limit theorem (CLT)

Law of large numbers (LLN)

II. Applications

Arbitrage and martingales, risk neutral measures.

Complete markets and discrete option pricing.

The binary tree model of Cox, Ross and Rubinstein for European and American option

pricing (discrete Black-Scholes)

Dividends in the binomial models

Trinomial model (incomplete markets

Convergence of the CRR to the Black-Scholes model

Learning outcomes:

Identify and solve problems involving conditional expectation. Demonstrate a thorough

understanding of the Cox-Ross-Rubinstein binomial model and apply it to option pricing

problems. Demonstrate an understanding of the role of the risk-neutral pricing measure.

Demonstrate an understanding of the main aspects of discrete-time martingale theory.

Demonstrate an understanding of the Doob's Optional Stopping Theorem. Critical understanding


of the Cox-Ross-Rubinstein model. Conceptual understanding of the role of the risk-neutral pricing

measure. Conceptual understanding of the role of equivalent martingale measures in financial

mathematics. Conceptual understanding of the Optional Stopping problem.

Assessment method: Examination 100%.

Stochastic Analysis in Finance I (SAFI, 7.5 points S1)

Lecturer : Istvan Gyongy

Delivery: Steady

Aims: This course will provide the key mathematical ideas which are used in derivative pricing. By

spending a significant proportion of time on this particular topic it is hoped that the students

develop a good understanding of the mathematics. This will provide a rigorous framework for the

derivative pricing course enabling students to understand where the assumptions in the models

break down.

Syllabus:

Continuous time processes: basic ideas, filtration, conditional expectation, stopping times.

Continuous parameter martingales, sub- and super-martingales, martingale inequalities,

optional sampling.

Wiener martingale, stochastic integral, the Itô calculus and some applications.

Multi-dimensional Wiener process, multi-dimensional Itô formula.

Stochastic differential equations

Change of measure, Girsanov's theorem, equivalent martingale measures and arbitrage.

Representation of martingales and the Ornstein-Uhlenbeck process.

Learning outcomes:

Demonstrate an understanding of continuous time stochastic processes. Knowledge of the main

results and basic applications of stochastic Itô calculus. Ability to understand stochastic

differential equations (SDE's). Ability to understand equivalent measures and in particular

Girsanov's theorem. Conceptual understanding of martingales in continuous time. Conceptual

understanding of the stochastic Itô integral and Itô formula.

Assessment method: Examination 100% (Stochastic Analysis in Finance I and II will be examined

together).

Fundamentals of Optimization (FuO, 10 points S1)

Lecturer : Jacek Gondzio

Delivery: Steady

Aims: Many management decision problems can be formulated as Linear Programming problems.

This core course introduces the theory and application of this important optimization technique.

Syllabus: Convexity; Linear Programming: model formulation, simplex method: tableau form,

revised SM, geometric interpretation, sensitivity analysis; Extensions and applications of LP: goal


programming, data envelopment analysis (DEA), transportation problems, piecewise linear

objective, cutting plane methods; Duality in LP, Lagrangian Relaxation; Solving LP problems using

commercial mathematical programming software; Public domain software for optimization, NEOS.

Learning outcomes: Knowledge of basic mathematical programming techniques. Ability to

formulate decision problems as mathematical programmes. Ability to solve simple problems and

to use the right software to solve complicated problems.

Assessment method: Coursework 15%, Examination 85%.

Finance, Risk and Uncertainty (FRU, 10 points S1)

Lecturer : John Davies and Andrew Marshall

Delivery: Steady

Aims: To provide key concepts in Finance which are integral part of the theory of Financial Math-

ematics.

Syllabus:

Identify the main building blocks of modern finance theory;

Use compounding and discounting to evaluate financial proposals;

Determine the net present value and internal rate of return of investment proposals;

Explain the merits of the net present value rule as an investment criteria;

Appreciate the merits as well as the limitations of the internal rate of return as an

investment criterion;

Determine the cost of borrowing and evaluate financing proposals; - Define and measure

risk;

Evaluate the risk of investment and securities;

Construct portfolios to reduce risk exposure;

Differentiate between efficient and inefficient portfolios;

Develop the capital asset pricing model;

Explain and critically evaluate capital market theory;

Understand the role of beta as a measure of risk;

Undertake the evaluation of capital budget proposals;

Derive the implications of the capital asset pricing model for security analysis and

corporate financial management;

Explore the behaviour of the prices of financial assets and returns in competitive capital

markets;

Critically appraise the efficiency of the capital market;

Learning outcomes:

Skills in structuring and taking decisions. Appreciation of the need to evaluate decision criteria in

relation to the objectives of the decision. Ability to construct and manipulate mathematical

models. Appreciation of the role of abstract reasoning in understanding and approaching practical

problems. Ability to assess and test theories and hypotheses on the basis of empirical evidence.

Assessment method: Assessment 30%; Examination 70%.


Stochastic Analysis in Finance II (SAFII, 7.5 points S2)

Lecturer : Istvan Gyongy

Delivery: Steady

Aims: This course will develop in detail the mathematical ideas which are used in derivative

pricing. By spending a significant proportion of time on this particular topic it is hoped that the

students develop a good understanding of the mathematics. This will provide a rigorous

framework for the derivative pricing course enabling students to understand where the

assumptions in the models break down.

Syllabus:

The Black-Scholes model, self-financing strategies, pricing and hedging options, European

and American options.

Option pricing and partial differential equations; Kolmogorov equations

Further topics: dividends, reflection principle, exotic options, options involving more than

one risky asset, stochastic volatility models

Learning outcomes:

Ability to apply the theory of stochastic calculus to problems involving vanilla options.

Understanding of the martingale representation theorem and its role in financial applications.

Ability to apply the theory of stochastic calculus to problems involving exotic options. Conceptual

understanding of the role of martingales in the theory of derivative pricing. Conceptual

understanding of the role of equivalent martingale measures in financial mathematics. Conceptual

understanding of the stochastic Itô integral and the connection to self-financing strategies

Assessment method: Examination 100% (Stochastic Analysis in Finance I and II will be examined

together).

Risk-Neutral Asset Pricing (APr, 15 points S2)

Lecturer : Miklos Rasonyi and Sotirios Sabanis

Delivery: Steady

Aims: To provide solid mathematical foundations for pricing derivative products in financial

markets, highlighting the points where the idealized and the realistic diverge.

Syllabus:

Introduction to bonds, futures and options.

Risk-neutral valuation of contingent claims. Pricing PDEs.

Some important option types in the Black-Scholes setting. Parameter sensitivity (Greeks).

Incomplete markets, pricing and hedging.

The term structure of interest rates: short rate models (Vasicek, CIR) and the HJM

framework.

Pricing of credit derivatives.


Learning outcomes:

Familiarity with the fundamental tools of no-arbitrage pricing (Girsanov change of measure,

martingale representation). Knowledge of most important option types (European, American,

exotic). Familiarity with the PDE methodology for computing option prices. Understanding the

essentials of short rate and forward rate models (i.e. HJM). Familiarity with the basic credit

derivatives and with the problems in their pricing (default sensitivity).


Simulation (FSim, 10 points S2)

Lecturer : Istvan Gyongy and Miklos Rasonyi

Delivery: Steady

Aims: Introduction to Monte Carlo methods and random number generation. Use of simulation for

option pricing. Variance reduction methods and introduction to numerical schemes for Stochastic

Differential Equations (SDEs).

Syllabus: Random number generation; pseudorandom numbers, inversion method,

acceptance/rejection method, Box-Muller method, basic Monte Carlo, quasi Monte Carlo, variance

reduction techniques, simulating Brownian paths, strong and weak approximations of solutions to

SDEs, Euler's approximations, Milstein's scheme, Order of accuracy of the approximations, Higher

order schemes, accelerated convergence, weak approximations of SDEs, Option price sensitivities.

Learning outcomes: Ability to describe how to simulate random variables of a given law.

Understanding of the main variance-reduction methods. Familiarity with simulating paths of

Brownian motion. Developing a critical awareness of the nature of random simulation and the

types of errors associated with these approximations. Familiarity with numerical schemes for

simulating solutions of SDEs. Ability to apply simple higher order schemes.


Optimization Methods in Finance (OMF, 15 points S2)

Lecturer : Peter Richtarik

Delivery: Steady

Aims: This course will demonstrate how recent advances in optimization modelling, algorithms

and software can be applied to solve practical problems in computational finance. Previous

exposure to optimization theory and methods is not assumed.

Syllabus: Linear Programming: Computing a dedicated bond portfolio, Asset pricing and arbitrage.

Quadratic Programming: Portfolio optimization (Markowitz model). Conic Optimization:

Approximating covariance matrices. Integer Programming: Constructing an index fund. Stochastic

Programming: Asset/Liability management. The role of simulation in modelling and solving

stochastic programming problems. Risk Modelling: Value-at-Risk, Conditional Value-at-Risk.

Robust Optimization: Robust portfolio selection.


Learning outcomes: Ability to formulate and solve practical problems arising in finance using

modern optimization methods and software. Familiarity with different formulations, their

purpose, strengths and weaknesses. Awareness of different approaches to risk modelling.

Assessment method: Continuous assessment 25%, Examination 75%.

8.2 Research-Linked Topics

The Research-Linked Topics consist of two short courses with a grand total of 5 points. They are

taken as guided reading courses in weeks 10-13 of semester 1 and in weeks 7-12 of semester 2. In

each semester, students choose 1 topic from a list which may vary from year to year. The topics

are assessed by either oral presentation and/or short written essay. A topic is assessed in week 13

of semester 1, and another topic is assessed in week 11 of semester 2. The combined weight of the

topics is 5 points, that is, each is worth 2.75 points.

8.3 Optional courses

Students must choose optional courses with a total of 25 points. The list of optional courses below

is provisional at this stage. Some courses may not be available in a specific year.

Introduction to Java Programming (IJP, 10 points S1)

Lecturer : Paul Anderson (Informatics)

Delivery: Steady

Aims: The study of Informatics generally involves the formation of hypotheses and theories which

can then be tested through the creation of computer models. In order to create these models,

students need to be able to write their own computer programs as well as use pre-existing special

purpose systems and tools. This course is intended to provide students who do not already have

significant computing experience, with the ability and confidence to use Java as their

programming tool for their summer project work.

This is a lab-based course, supported by the BlueJ book. Students will also follow an accompanying

series of recorded lectures online, and carry out additional programming work outside the

timetabled laboratory sessions.

Syllabus: Objects and classes. Understanding class definitions. Object interaction. Grouping objects.

Designing classes. Improving structure with inheritance. Building graphical user interfaces.

Handling errors. Designing applications.

Learning outcomes: 1 - Students will be able to state, in writing and verbally, basic principles of

object-oriented software design. 2 - Given an object oriented design as a diagram or textual

description, students will be able to evaluate the quality of that design and discuss its strengths

and weaknesses with respect to its stated purpose. 3 - Students will be able to design an object

oriented software solution to a problem using diagrammatic and textual representations. 4 -

Students will be able to implement an object oriented design in the Java language. 5 - Students

will be able to relate the syntax of the Java language to its semantics, and analyse the result of


executing fragments of Java syntax. 6 - Given a Java program, students will be able to explain, in

writing and verbally, what would happen when that program is executed, and identify bugs which

would prevent it executing as described in the program documentation. 7 - Given a Java program

and a debugging tool, students will be able to identify and correct bugs which prevent the program

from functioning as intended. 8 - Students will be to write documentation in Javadoc style to

explain the design and implementation of their own code, or example code which is supplied to

them. 9 - Students will be able to use the Java development environments BlueJ, Eclipse or

NetBeans. 10 - Students will be able to integrate library code with their own programs using

appropriate software tools. 11 - Students will be able to use online technical documentation to

solve implementation problems as they arise during software development. 12 - Students will be

able to describe stages in the software development process and the identify software tools which

are used to support these stages.

Assessment method: Continuous assessment 100%.

Parallel Numerical Algorithms (PNA, 10 points S1)

Lecturer : Chris Maynard (EPCC)

Delivery: Steady

Aims: This course looks at efficient parallel algorithms for performing commonly required tasks in

computing. It introduces basic algorithmic complexity theory and parallel scaling with reference

to the parallel implementation and scaling of fundamental computational patterns. Syllabus: The

demand for performance of scientific applications as the driver for massive parallelism in

computational science is reviewed. Basic algorithmic complexity theory is described, and parallel

scaling introduced. Computational patterns, sometimes known as the "seven dwarfs" and how

they are implemented in serial and parallel are described, how they scale, and which applications

use them. The use of libraries such as ScaLAPACK and PETSc are reviewed. Topics include:

Computational science as the third methodology; Fundamentals of algorithmic complexity O(N)

etc; Basic numerics, floating-point representation and exceptions; Complexity theory and parallel

scaling analysis; weak and strong scaling); Implementing parallelism in the "seven dwarfs", scaling

and example applications; N-body/particle methods, Simple ODEs, Dense Linear Algebra,

algorithms and libraries (LAPACK); Sparse Linear Algebra; PDEs, BVPs and their solution

(pollution problem), IVPs and implicit methods); Spectral methods; FFW and applications);

Structured grids; Unstructured grids; Monte Carlo methods; Verification.

Learning outcomes: On completion of this course students should be able to: explain why

computer simulation is an essential technique in many areas of science, and understand its

advantages and limitations; Explain how real-valued quantities are represented on a computer as

floating-point variables.; Discuss the various sources of error relevant for computational

simulation.; Explain when different methods (particle, grid, stationary, time dependent) are

applicable, and compare the strengths and weaknesses of different parallelisation strategies.;

Convert simple partial differential equations into numerical form.; Select and implement the most

appropriate method for solving a given system of linear equations.; Use standard numerical

libraries in their own codes.; Diagnose when a numerical algorithm may be failing due to limited

machine precision or floating point exceptions.



Nonlinear Optimization (NO, 10 points S2)

Lecturers: Coralia Cartis and Roger Fletcher

Delivery: Steady

Aims: The solution of optimal decision-making and engineering design problems in which the

objective and constraints are nonlinear functions of potentially (very) many variables is required

on an everyday basis in the commercial and academic worlds. A closely-related subject is the

solution of nonlinear systems of equations, also referred to as least-squares or data fitting

problems that occur in almost every instance where observations or measurements are available

for modelling a continuous process or phenomenon, such as in weather forecasting. The

mathematical analysis of such problems and study of the classical methods for their solution are

fundamental for understanding both practical methods of solution and the nature of the solution

which may be obtained. Thus it imparts knowledge and insight into the optimal choice of available

methods (software) or ability to develop such techniques for the practical problems at hand.

Syllabus: Linesearch and trust-region methods for unconstrained optimization problems (steepest

descent, Newton's method); conjugate gradient method; linear and nonlinear least-squares. First

and second-order optimality conditions for constrained optimization problems; overview of

methods for constrained problems (active-set methods, sequential quadratic programming,

interior point methods, penalty methods, filter met1hods).

Learning outcomes: Understanding the construction and main solution ideas for nonlinear

optimization problems. Ability to assess the quality of available methods and solutions for such

problems, as well as to potentially develop such optimization techniques and implementations.

Assessment method: Examination 70%, Coursework 30%.

Dynamic and Integer Programming (DIP, 10 points S1)

Lecturer : Andreas Grothey and Ken McKinnon

Delivery: Steady

Aims: Dynamic programming is a neat way of solving sequential decision optimization problems.

The course therefore complements the core course on Mathematical Programming. Integer

Programming provides a general method of solving problems with logical constraints. Syllabus:

Dynamic Programming: Sequential decision processes. Principle of optimality. Applications:

network, inventory, option pricing, resource allocation problem, knapsack problems. Stochastic

problems. Lagrangian relaxation: The Lagrangian Dual, ¯nding approximate solutions and bounds.

Integer programming: modelling, relaxations, unimodularity, Branch and Bound, Gomory cuts,

knapsack problems.

Learning outcomes: Ability to formulate and solve a sequential decision optimization problem.

Ability to formulate and solve optimization problems with integer variables.



Game Theory (DIP, 5 points S1)

Lecturer : Peter Richtarik

Delivery: Steady

Aims: Game theory is concerned with mathematical modelling of behaviour of competitive

strategic situations in which success of strategic choices of one individual depends on the choices

of others.

Syllabus: Worst-case analysis, robust solutions, saddle points, mixed and pure Nash equilibria for

matrix games, dominant strategies. Two-person zero-sum games: minimax and maximin, LP

duality. Nash-Cournot equilibria, single commodity market and production and price in Monopoly,

Oligopoly and Perfect Competition situations. Stability of equilibria. Continuous evolution. Payoff

region, independent strategy region, cooperative region, Pareto optimal region. Repeated games:

Brown's method, Nash equilibria. Grim and Tit-for-Tat strategies. Folk theorem. Transferable or

non-transferable payoffs. Coalition stability, core of an n-player game. Fisher's equilibrium

argument.

Learning outcomes: Understanding of basic game solution concepts. Ability to compute Nash

equilibria of two-person zero-sum games by linear programming. Ability to analyse strategic

problems in competitive commercial situations.


Credit Scoring and Data Mining (CSDM, 10 points S2)

Lecturers: David Edelman and Nick Radcliffe

Delivery: Intensive

Aims: Understanding how large data sets can be used to model customer behaviour. How such

data is gathered, stored and interrogated and its use to cluster, segment and score individuals. The

aim is to look at the largest applications in more detail. Credit scoring is the process of deciding,

whether or not to grant or extend a loan. Sophisticated mathematical and statistical models have

been developed to assist in such decision problems.

Syllabus: Large scale databases - data warehouse and data archives; statistical approaches

(clustering, discrimination, regression); non statistical approaches, including neural networks and

genetic algorithms; commercial software; applications such as clustering, segmenting and scoring.

Introduction to credit scoring. Setting up a scoring system. Statistical techniques used in credit

scoring. Other approaches to credit scoring. Use of behavioural scoring. Techniques used in

behavioural scoring systems. Monitoring and updating scoring systems. Developments in scoring

systems.

Learning outcomes: Understanding of statistical and alternative methods of constructing scoring

rules. Understanding how to process data prior to model building. Ability to assess and monitor a


scorecard. Awareness of current and new applications of credit scoring techniques. Understanding

of real life application of data mining, including clustering, segmentation and scoring.


Financial Risk Management (FRM, 10 points S2)

Lecturers: Emmanuel Fragniere, Akimou Osse and Nils Tuchschmid

Delivery: Intensive

Aims: Risk is a key factor in many financial decisions. The ability to assess risk and hedge the

decision against it needs to be translated into quantitative measures.

Syllabus:

Introduction:

Analysis of risks in the banking sector, Financial engineering, the parametric model-based

approach, correlations and diversifcation of risk. Basel II: capital adequacy based on

mathematical models, Interest risk and ALM (Asset and Liability Management).

Market Risk:

Value at Risk models (Historical simulation, Monte Carlo simulation, RiskMetrics,

Markowitz mean-variance, the Sharpe ratio, i.e. CAPM model, conditional VAR), Portfolio

hedging (Option pricing models, Trading systems, Delta, Gamma and other types of

hedging).

Credit risk:

Rating/Pricing models, Expected and unexpected losses, Credit risk modelling.

Operational risk:

The nature of qualitative risks, The method of loss distribution, The scorecard approach.

Model risk:

Danger of applying models in inappropriate situations.

Stress testing:

Extreme event theory and simulating rare and catastrophic events.

Conclusions:

Towards integrated risk management.

Learning outcomes: Ability to assess risk in ¯nancial decision problems and construct appropriate

financial risk model.


Risk Analysis (RA, 5 points S2)

Lecturers: Ken McKinnon, Richard Knight and Simon Smith

Delivery: Steady


Aims: Risk analysis deals with the assessment and management of the risk form unlikely but costly

events. This course will introduce the risk analysis methodology.

Syllabus: Sources of risk; attitudes to risk; approaches to assessing the probability and cost of risky

events and methods of controlling risk.

Learning outcomes: Understanding of approaches to assess and minimize risk in real life

applications.


Stochastic Modelling (SM, 10 points S2)

Lecturer : Burak Buke

Delivery: Steady

Aims: Uncertainty and randomness serve to make decision problems encountered in industry non-

trivial. Uncertainty is quantified through the study of probability. This course aims to cover an

important area of Applied Probability, namely Markov Processes. The second part of the course

deals with continuous time processes, including the application to queueing problems which form

the subject of many OR investigations.

Syllabus: Discrete state, discrete time Markov Chains: Probability transition matrix. Solution of a

two-state chain using difference equations, generating function, diagonalisation (distinct eigenval-

ues only). Classification of states, transience, recurrence, periodicity, irreducibility. Unrestricted

random walk in 1 dimension. Mean first passage and recurrence times. Absorption probabilities.

Random walk with absorbing barrier(s). Stationary distributions and global balance. Limiting

distribution. Time reversibility in equilibrium and local balance. Above illustrated with examples

taken from Genetics, Queues, Management Science. Birth and Death Processes: Homogeneous

Poisson process. Heterogeneous and Compound Poisson processes. Use of generating functions to

understand behaviour of B-D processes. Continuous time Markov Chains (discrete state): The

infinitesimal generator (Q matrix). Forward and backward differential equations. Solution by

diagonalisation. Stationary solutions. Time reversibility. Embedded Chains.

Learning outcomes: Knowledge of behaviour of discrete-state discrete and continuous time

Markov Chains. Where appropriate, the ability to formulate real-life problems as a Markov

Process. Understanding of elementary queueing theory.

Assessment method: Coursework 15%, Examination 85%.

Combinatorial Optimization (CO, 5 points S2)

Lecturer : Jonathan Thompson

Delivery: Intensive

Aims: This course will discuss both mathematical programming and heuristic approaches to

solving combinatorial optimization problems.


Syllabus: This course will address both optimal and heuristic approaches - cutting plane, branch-

and-bound, branch-and-cut, Lagrangian relaxation, local search, simulated annealing, tabu search,

genetic algorithms, and neural networks - to solving combinatorial optimization problems such as

production planning and scheduling, operational management of distribution systems,

timetabling, location and layout of facilities, routing and scheduling of vehicles and crews, etc.

Learning outcomes: Ability to formulate a wide range of management problems that can be solved

to optimality by classical combinatorial optimization techniques and the knowledge of alternative

solution approaches such as metaheuristics that can find nearly optimal solutions. Awareness how

difficult some practical optimization problems can be and the complex role performed by

managers.


Large Scale Optimization (LSO, 10 points S2)

Lecturer : Jacek Gondzio

Delivery: Steady

Aims: The modelling of real life problems often requires very large detailed optimization models,

and the efficient solution of such problems is usually the key to the success of optimization in

practice. Such models can be solved only if the optimization algorithms are designed with care for

efficiency in data management and execution time.

Syllabus: Implementation of the revised simplex method; interior point methods for linear,

quadratic and nonlinear optimization; sparse matrix techniques in optimization; decomposition

methods: Benders and Dantzig-Wolfe decompositions; Newton method, self-concordant barriers,

semidefinite programming; Applications of mathematical programming in finance,

telecommunications, energy sector.

Learning outcomes: Understanding of practical methods of large-scale optimization and their

modern implementations. Awareness of the practical complexity of the problems. Ability to model

real-life problems as optimization problems. Ability to develop and implement an optimization

technique.


Stochastic Optimization (SO, 5 points S2)

Lecturer : Roberto Rossi

Delivery: Steady

Aims: Stochastic optimization deals with the optimization problems with uncertain data. The

modelling objective is to find the best way to hedge against the resulting risk. The applications

considered are: portfolio analysis, strategic planning, sequential sampling and production

problems.


Syllabus: Decision analysis and model building; Expected value of information; Sequential

sampling problems; Markov decision processes; Applications of Markov decision processes in

finance and other areas.

Learning outcomes: Ability to formulate optimization problems in the presence of uncertainty.

Knowledge of techniques that can be used to solve such problems.

Assessment method: Examination 85%, Coursework 15% .

International Money and Finance (IMF, 10 points S2)

Lecturer: Jonathan Thomas

Delivery: Steady

Aims: The course will consider: theoretical and applied aspects of exchange rate determination,

such as explaining exchange rate volatility, equilibrium and forecastability issues; the economics

of fixed exchange rates in terms of the target zone literature, the speculative attack literature and

contagion; some of the so-called puzzles in international finance, such as the Feldstein-Horioka

and home bias puzzles and the role of the gravity model in explaining international trade patterns;

the evolution of the European Monetary System, from ERM to euro, and the role of the ECB in the

operation of monetary policy in the euro area.

Learning outcomes: The course aims to cover the major recent developments in the field of

International Money and Finance. The course consists of a blend of theoretical, applied

econometric and institutional material.

Assessment method: Examination 100%

Advanced Time Series Econometrics (ATSE, 10 points S2)

Lecturer: Jonathan Thomas

Delivery: Steady

Aims: This module explores further topics in time series econometrics. Students will be introduced

to various tools that are part of the basic econometric training of professional economists. The

course is intended for students who want to be professional economists or who want to go on to

PhD study, i.e., at aspiring economists rather than aspiring econometricians. Material covered will

include modelling volatility and cointegration and error-correction models.

Assessment method: Examination 100%


9 Facilities

9.1 James Clerk Maxwell Building

Your right to be in the building and means of access are as follows

08:00 - 18:00 Monday-Friday: No restriction on access

18:00 - 21:00 Monday-Friday and 09:00 - 17:00 Saturday and Sunday: You are allowed in

the building and your student card will give you access.

At all other times you are not permitted to be in the building and your student card (even

with the PIN) should not give you access.

If you are found in the building when you are not permitted to be then you must leave when asked

to do so.

9.2 University Library

The University of Edinburgh has excellent library facilities. The libraries which are most likely to

be relevant for this programme are:

Noreen and Kenneth Murray Library, King's Buildings, West Mains Road, Edinburgh EH9

3JF. Tel: 0131 650 5784

Main Library, George Square, Edinburgh, EH8 9LJ, tel: 0131 650 3409.

For the opening hours see:

http://www.ed.ac.uk/schools-departments/information-services/library-museum-gallery/using-

libraries

9.3 MSc Workroom

The MSc workroom is 5210 in JCMB. Near 5210 is the “kitchen area" of the School of Mathematics

with a sink, fridge, microwave, cupboards and kettle for the use of all occupants of the 5210 room.

9.4 Careers service

The University of Edinburgh Careers Service offers advice, information and practical help in all

matters relating to the development of students' future work or study plans. The Careers Service

offices are at:

Weir Building, West Mains Road, Edinburgh EH9 3JY Tel: 0131 650 5773 Fax: 0131 650

6704 Email: [email protected].

33 Buccleuch Place, Edinburgh, EH8 9JS Tel: 0131 650 4670 Fax: 0131 650 4479 Email:

[email protected].

For the opening hours see the Careers Service web site: http://www.careers.ed.ac.uk/

http://www.ed.ac.uk/schools-departments/information-services/library-museum-gallery/using-libraries

http://www.ed.ac.uk/schools-departments/information-services/library-museum-gallery/using-libraries


9.5 Computer facilities

All students have a University email address that allows them to be contacted from anywhere in

the world. Students are required to check regularly for email sent to this address, since this

is the formal means of communication with students.

The University of Edinburgh provides free wireless internet (WiFi) in most buildings. You need to

register before you can use it with your wireless-enabled device. Further information will be

distributed during Induction Week.

Students should use the MSc computing room (5205). An introduction to computing facilities will

be provided during Induction Week when the full range of facilities will be explained.

Any general hardware/software problems should be reported to [email protected].

9.6 Travel

A shuttle bus runs at regular times between the George Square campus and the Kings Buildings

campus of the University of Edinburgh.

http://www.ed.ac.uk/staff-students/students/shuttlebus

There are several scheduled bus services which run from the city centre to the Kings Buildings

campus:

The number 42 runs every 20 minutes from George IV Bridge to Kings Buildings.

Other buses (3, 7, 8, 31) run along a parallel route from North Bridge and Nicholson Street to

Craigmillar Park and Cameron Toll. From there it is a 5 minute walk to the Kings Buildings

campus.

For more info about Lothian buses see http://lothianbuses.com/

http://www.ed.ac.uk/staff-students/students/shuttlebus

http://lothianbuses.com/


Appendix I – Institute for Academic Development Flyer

Institute for Academic Development The Institute for Academic Development (IAD) provides a number of workshops and resources for University of Edinburgh postgraduate taught students, to help you gain the skills, knowledge, and confidence needed for studying at postgraduate level. The workshops are free of charge to students and are organised by the IAD or in conjunction with the Schools and College. Workshops can be booked via MyEd or the IAD website. Workshops Workshops may vary from the list below and Schools may offer additional workshops, arranged in conjunction with the IAD:

Study Skills

Dissertation Writing & Planning

Presentation & Speaking Skills

Exam Preparation

Poster production

Project Planning & Ethics

Problem Solving Resources There are various resources available on the IAD website, including:

Preparing for your studies

Studying at postgraduate level

Developing your English

Literature searching

Managing research workloads

Writing a postgraduate level

Assignments: planning and drafting

Critical thinking

Using digital media

Guides and codes Contact Details: Institute for Academic for Academic Development 7 Bristo Square Edinburgh EH8 9AL Blog: http://iad4masters.wordpress.com/ Website: www.ed.ac.uk/iad/postgraduates Email: [email protected]

http://iad4masters.wordpress.com/

http://www.ed.ac.uk/iad/postgraduates


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MSc in Financial Modelling and Optimization - info.maths · 4.2 Optional courses ... MSc'', or simply the ... recent paper, making its results more accessible and putting them in