MA5041 - Statistical Methods and Modelling Markets (2022/23)
|Module specification||Module approved to run in 2022/23|
|Module title||Statistical Methods and Modelling Markets|
|Module level||Intermediate (05)|
|Credit rating for module||30|
|School||School of Computing and Digital Media|
|Total study hours||300|
|Running in 2022/23(Please note that module timeslots are subject to change)||
The module covers mathematical and statistical modelling techniques that are applied in making decisions in areas of finance. It also enables the student to investigate real-life statistical data.
This module introduces important financial concepts and develops statistical modelling techniques. Statistical regression models are applied to financial data (e.g., credit scoring, default time analysis) and mathematical modelling of stock and option prices is investigated. A selection of suitable software (e.g. Excel, R, SPSS) will enable students to analyse data in order to make informed decisions. The students will develop skills in statistical and mathematical modelling of real data to aid future employability.
Analysis of variance and covariance. LO2, LO3
Multivariate linear regression and logistic regression analysis. LO2, LO3
Survival analysis for time to event data. LO2, LO3
Models for stock and option trees and pricing. LO1
Stock price investigation using Geometric Brownian motion. LO1,LO3,LO4
Balance of independent study and scheduled teaching activity
Students’ learning is directed via face-to-face learning activities that include lectures, seminars and practicals involving case studies and real data analysis. There is full provision of documents related to the module in electronic format on the University virtual learning environment that can be accessed by students at all times. The documents include module specs, staff contact details, surgery/office hours and regular notices, lecture notes, slides, practical sheets on financial data analysis, real financial data sets, the coursework, and examples of test and exam. Students are motivated to analyse real financial and statistical data sets made available to them using statistical packages.
Students are encouraged to install the statistics packages on their own PC or laptop to improve their expertise with the package and to complete practicals and the coursework.
On successful completion of this module, students should be able to:
LO1. Demonstrate an understanding of mathematical modelling applied to stock and option pricing
LO2. Use statistical techniques applied to data for inference, prediction and credit scoring
LO3. Use an appropriate statistical package (such as R, SPSS, Excel) to fit statistical
models to data; and investigate and interpret the results.
LO4. Understand the practical application and implications of statistical and mathematical modelling
of real financial data
The assessment involves a test, a coursework and an exam.
The test will assess learning outcomes LO2 and LO3.
The coursework will assess learning outcomes LO1, LO3 and LO4.
The exam will assess learning outcomes LO1, LO2 and LO3.
Formative assessments are given every week during the tutorial/seminar sessions while summative assessment feedback will be made available at the end of each test/coursework.
Stampfli, J and Goodman, V (2001) The Mathematics of Finance: Modeling and Hedging, Brook/Cole Field, A. (2009) Discovering statistics using SPSS. 3rd edition. Sage.
Wilmott, P (2001), Paul Wilmott Introduces Quantitative finance, JohnWiley
Ross, S (2003) An Elementary Introduction to Mathematical Finance, Options and other Topics, CUP
Daly, F. Hand, D., Jones, Lunn, M. and McConway, K. (1995) Elements of Statistics, Addison Wesley, ISBN 0-201-42278-6.
Klein, J.P. and Moeschberger, M.L. (2003) Survival Analysis: Techniques for Censored and Truncated Data, 2nd edition, Springer