MA6041 - Financial Modelling and Forecasting (2018/19)
|Module specification||Module approved to run in 2018/19, but may be subject to modification|
|Module title||Financial Modelling and Forecasting|
|Module level||Honours (06)|
|Credit rating for module||30|
|School||School of Computing and Digital Media|
|Total study hours||300|
|Running in 2018/19||
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 be developing problem solving skills. For each given problem, the process of dealing with it includes, searching for appropriate data sets, establishing the right statistical/financial techniques to use, fitting appropriate models, critically appraising the models using diagnostic model tools and finally interpreting the models and drawing conclusions.
Prior learning requirements
Introductory Financial Math and Statistics
Stochastic processes their use in time series analysis. LO1
ARIMA, Regression, and state space statistical models used for forecasting.
Stochastic volatility models. LO2
Stochastic differential equations applied to the Black-Scholes formulas and the ‘Greeks’.
Exotic and Path-Dependent Options. LO3
Portfolio Management – Diversification; Capital Asset Pricing Model. LO4
Balance of independent study and scheduled teaching activity
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 enable students to use problem solving skills, whereas the student-led sessions are designed to give students the opportunity for independent learning as well as collaboration. Materials for learning are provided through an integrated learning environment (currently WebLearn) supplemented by online sources and text books. 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 a stochastic process and how it can be applied to solve real problems in financial and other scientific commercial environments.
LO2: Understand the fundamental ideas of Statistical modelling including fitting, selecting and critically assessing a model.
LO3 : Interpret results and communicate effectively through report writing and presentation
LO4: 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 1-4 and an exam (40%) covering LO1-LO3. The coursework will be done in groups involving individual inputs with one report from each group.
Reassessment Strategy for the group coursework.
Students who have reassessment opportunity in the Group Coursework will be required to work on the first sit coursework and submit it as an individual coursework on Weblearn.
Shumway R and Stoffer D (2010, 3rd Edition). Time series Analysis and its Applications. Springer.
Wilmott, P, (2006, 2nd Edition) Paul Wilmott on Quantitative Finance, John Wiley and Sons.
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.