module specification

The module introduces the students to financial forecasting using modern statistical modelling techniques. Its aim is to prepare the student for work in a quantitative commercial or scientific environment.  Students will require developing problem solving skills.  For each given problem, the process of dealing with it includes, searching for appropriate data set, establishing the right statistical/financial techniques to use,  fitting appropriate models,  critically appraise  the models using  diagnostic model tools and finally interpreting the models and draw conclusions.

The module aims to
1. Introduce stochastic process to build statistical models aiming is to represent reality.
2. Introduce the advantages and limitations of statistical modelling techniques.
3. Present statistical and financial techniques and solving problem tools to students.
4. Improve the student's communication skills through report writing and presentation.
5. Develop the student's ability to work effectively in-groups.
 

Stochastic processes their use in time series analysis.
ARIMA, Regression, and state space statistical models used for forecasting.
Stochastic volatility models.
Stochastic differential equations applied to the Black-Scholes formulas and the ‘Greeks’.
Exotic and Path-Dependent Options.
Portfolio Management – Diversification; Capital Asset Pricing Model.

The module will be delivered through a weekly 3-hour block consisting of a mixture of a lecture and a tutorial or workshop (in a computer lab). While the theory and methods will be covered during the lectures, the practice exercises and student-led group discussions will be carried out in tutorial and workshop sessions. The tutor-led sessions are intended to teach students the problem solving skills, whereas the student-led sessions are designed to train students the ability of independence as well as collaboration. Materials for learning are provided through main text books supplemented by online sources and an integrated learning environment (currently WebLearn). In addition to the timetabled classes, students are required to spend about 7 hours each week working individually and in their groups. Then the tutorial sessions and workshops will provide an ideal setting for students to meet up with their group members on regular basis and carry out their discussions and investigations.

On successful completion of the module, students should be able to
LO1 : Understand the concept of stochastic process and how can be apply to solve real problems in financial and more generally in other scientific commercial environments.
LO2 :  Understand the fundamental ideas of Statistical modelling including fitting, selecting and critically assess a model. 
L03 : Interpreting results and communicate effectively through report writing and presentation
L04 : Demonstrate effective collaboration when working in a group.

The assessment consists of an individual coursework (30% ) a group coursework (30% ) reflecting all the learning outcomes 1-5 and an exam (40%) covering LO1-LO3. The coursework will be done in groups involving individual inputs with one report from each group. The coursework is divided into two parts: Part 1 (20%) should be completed and handed in by week 13 and Part 2 (40%) by week 25

Cryer, J. (2008) Time Series Analysis With Applications in R.  Springer.
Higham, D, (2004) An Introduction to Financial Option Valuation: Mathematics, Stochastic and Computation,  Cambridge University Press
Hull , J. C (2006) Options, Futures and Other Derivatives, Prentice Hall
Makridakis, S, Wheelwright , S.C., Hyndman, R.J. (1998) Forecasting Methods and Applications, 3rd Edition,    Wiley.
Shumway R and Stoffer D. Time series Analysis and its Applications. Springer.
Wilmott, P, (2006, 2nd Edition) Paul Wilmott on Quantitative Finance, John Wiley and Sons.