Analysis and Computation for Finance

Module description

On this module, you will get the chance to use the popular computer package Matlab and other relevant modelling software. We will cover topics from linear algebra, differential equations, statistical modelling, stochastic differential equations and time series analysis, and use these to demonstrate the versatility and capabilities of such packages in the application of modern numerical modelling techniques. The background and skills you will obtain in this module will be useful in the Financial Mathematics module ECMM706 and in the dissertation ECMM720.

Full module specification

Module title:Analysis and Computation for Finance
Module code:ECMM703
Module level:M
Academic year:2014/5
Module lecturers:
  • Dr Timothy Jupp - Convenor
Module credit:15
ECTS value:


Duration of module: Duration (weeks) - term 1:


Module aims

Computer packages such as Matlab are playing an increasing role in implementing the models arising from theoretical ideas in mathematical finance.This module aims to give you an understanding of the modern methods of numerical approximation and financial modelling. Using Matlab and other relevant software, you will develop practical skills in the use of computers in financial modelling.

ILO: Module-specific skills

  • 1. demonstrate expertise in the use of Matlab and R widely used both inside and outside the academic community and be able to use these to model challenging mathematical problems.

ILO: Discipline-specific skills

  • 2. tackle a wide range of mathematical problems using modern numerical methods;
  • 3. model realistic situations and also understand the principles underlying the techniques and when they are applicable.

ILO: Personal and key skills

  • 4. show enhanced modelling, problem-solving and computing skills, and acquired tools that are widely used in financial modelling.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled learning and teaching activities36Lectures/supervised practical laboratory sessions/presentation of special topics
Guided independent study114Lecture and assessment preparation; wider reading

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Written exam – closed book502 hoursAllNone
Coursework – problem sheet 2: approximation tools8AllWritten
Coursework – problem sheet 3: numerical matrix algebra10AllWritten
Coursework – problem sheet 4: computational ODEs and PDEs10AllWritten
Coursework – problem sheet 5: statistical modelling12AllWritten
Coursework – problem sheet 6: time series10AllWritten

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
All aboveWritten exam (100%)AllAugust Ref/Def period

Re-assessment notes

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

Syllabus plan

- introduction to Matlab system and interface: matrix data objects; mathematical operations and functions;
- I/O control;
- programming;
- graphical tools;
- plotting and data representation;
- approximation techniques: curve �tting and related methods;
- numerical matrix algebra: review;
- numerical calculation of eigenvalues, eigenvectors, determinants and inversion;
- decompositions;
- special topics in numerical modelling: condition number;
- matrix nearness;
- Matlab numerical linear algebra tools;
- practical, numerical modelling using Matlab;
- computational ODEs, PDEs and dynamical systems: series,transforms,splines and interpolation;
- �nite differences, shooting methods and convergence;
- Matlab DE tools;
- practical use of Matlab;
- statistical modelling: introduction to statistical models, parametric versus non-parametric models;
- likelihood, Bayesian and resampling inferential approaches;
- Markov Chain Monte Carlo, (MCMC methods;
- examples of parametric models - linear and generalised linear models;
- examples of computer-intensive non parametric modelling;
- use of relevant software in practical data modelling;
- times series modelling: fundamentals;
- methods for time series analysis and forecasting.

Indicative learning resources - Basic reading


An Introduction To Numerical Methods: a MATLAB Approach, Kharab A. & Guenther R.B., Chapman & Hall, 2012, 518.0285 KHA, ISBN 978-1439868997

Data Analysis & Graphics using R, Maindonald J. & Braun J., 2nd edition, Cambridge University Press, 2007, 001.6424 MAI, ISBN 9780521861168

Computational statistics handbook with MATLAB, Martinez W.L. & Martinez A.R., Chapman & Hall, 2001, 519.50285 MAR, ISBN 000-1-584-88229-8

Time Series Analysis and its applications With R Examples, Shumway, R H, Stoffer, D S, 2nd, Springer Texts in Statistics, 2006, ISBN 978-0387293172

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