- Homepage
- Key Information
- Students
- Taught programmes (UG / PGT)
- Computer Science
- Engineering
- Geology (CSM)
- Mathematics (Exeter)
- Mathematics (Penryn)
- Mining and Minerals Engineering (CSM)
- Physics and Astronomy
- Renewable Energy
- Natural Sciences
- CSM Student and Staff Handbook

- Student Services and Procedures
- Student Support
- Events and Colloquia
- International Students
- Students as Change Agents (SACA)
- Student Staff Liaison Committees (SSLC)
- The Exeter Award
- Peer Support
- Skills Development
- Equality and Diversity
- Athena SWAN
- Outreach
- Living Systems Institute Webpage
- Alumni
- Info points and hubs

- Taught programmes (UG / PGT)
- Staff
- PGR
- Health and Safety
- Computer Support
- National Student Survey (NSS)
- Intranet Help
- College Website

## ECMM703 - Analysis and Computation for Finance (2015)

MODULE TITLE | Analysis and Computation for Finance | CREDIT VALUE | 15 |
---|---|---|---|

MODULE CODE | ECMM703 | MODULE CONVENER | Dr Tim Jupp (Coordinator) |

DURATION: TERM | 1 | 2 | 3 |
---|---|---|---|

DURATION: WEEKS | 11 weeks | 0 | 0 |

Number of Students Taking Module (anticipated) | 21 |
---|

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.

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.

On successful completion of this module, **you should be able to**:

**Module Specific Skills and Knowledge:**

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.

**Discipline Specific Skills and Knowledge:**

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.

**Personal and Key Transferable/ Employment Skills and Knowledge:**

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

- 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 ﬁtting 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;

- ﬁnite 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.

Scheduled Learning & Teaching Activities | 39.00 | Guided Independent Study | 114.00 | Placement / Study Abroad |
---|

Category | Hours of study time | Description |

Scheduled learning and teaching activities | 24 | Lectures |

Scheduled learning and teaching activities | 15 | Workshops |

Guided independent study | 114 | Guided independent study |

Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|

Not applicable | |||

Coursework | 50 | Written Exams | 50 | Practical Exams |
---|

Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|---|

Written exam – closed book | 50 | 2 hours | All | None |

Coursework – problem sheet 2: approximation tools | 8 | All | Written | |

Coursework – problem sheet 3: numerical matrix algebra | 10 | All | Written | |

Coursework – problem sheet 4: computational ODEs and PDEs | 10 | All | Written | |

Coursework – problem sheet 5: statistical modelling | 12 | All | Written | |

Coursework – problem sheet 6: time series | 10 | All | Written |

Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-reassessment |
---|---|---|---|

All above | Written exam (100%) | All | August Ref/Def period |

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.

information that you are expected to consult. Further guidance will be provided by the Module Convener

ELE – http://vle.exeter.ac.uk

Reading list for this module:

Type | Author | Title | Edition | Publisher | Year | ISBN | Search |
---|---|---|---|---|---|---|---|

Set | Kharab A. & Guenther R.B. | An Introduction To Numerical Methods: a MATLAB Approach | Chapman & Hall | 2012 | 978-1439868997 | [Library] | |

Set | Maindonald J. & Braun J. | Data Analysis & Graphics using R | 2nd edition | Cambridge University Press | 2007 | 9780521861168 | [Library] |

Set | Martinez W.L. & Martinez A.R. | Computational statistics handbook with MATLAB | Chapman & Hall | 2001 | 000-1-584-88229-8 | [Library] | |

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

CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
---|---|---|---|

PRE-REQUISITE MODULES | None |
---|---|

CO-REQUISITE MODULES | None |

NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
---|---|---|---|

ORIGIN DATE | Friday 09 January 2015 | LAST REVISION DATE | Friday 09 January 2015 |

KEY WORDS SEARCH | Linear algebra; differential equations; statistical modelling; time series analysis; Matlab; R. |
---|