MA5040 - Financial Mathematics with Statistics 2 (2018/19)
|Module specification||Module approved to run in 2018/19|
|Module status||DELETED (This module is no longer running)|
|Module title||Financial Mathematics with Statistics 2|
|Module level||Intermediate (05)|
|Credit rating for module||30|
|School||School of Computing and Digital Media|
|Total study hours||300|
|Running in 2018/19||No instances running in the year|
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 financial problems.
This module introduces important financial concepts and develops the mathematical and statistical modelling techniques to solve problems in the area of finance. 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 (eg. Excel, R and SPSS) will enable students to analyse financial data in order to make informed financial decisions. The students will develop skills in statistical and mathematical modelling of real financial data to aid future employability.
Investigation of different distributions appropriate for modelling financial data. Linear regression analysis for predicting financial variables and logistic regression analysis for credit scoring. Survival analysis for time to default analysis. Models for stock and option pricing. Geometric brownian motion. Stock and option trees
Learning and teaching
The module approach to blended learning is as follows.
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 all the time. The documents include module specs, staff contact details, surgery/office hours and regular noticeboards, lecture notes and/or slides, practical sheets on financial data analysis, real financial data sets, the coursework, and an example of a test and an exam. Students are motivated to analyse real financial and statistical data sets made available to them using statistical packages (e.g. SPSS and R).
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. Understand the different probability distributions appropriate for a target financial response variable
LO3. Understand regression analysis applied to financial data for prediction, credit scoring and default time analysis.
LO4. Use an appropriate statistical package (such as R, SPSS, Excel) to fit mathematical and statistical
models to financial data; and investigate and interpret the results.
LO5. 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 LO1 and LO2.
The coursework will assess learning outcomes LO1 and LO5.
The exam will assess learning outcomes LO3 and LO4.
1) Daly, F. Hand, D., Jones, Lunn, M. and McConway, K. (1995) Elements of Statistics, Addison Wesley, ISBN 0-201-42278-6.
2) Crawley, M.J. (2005) Statistics: An introduction using R. Wiley.
3) Field, A. (2009) Discovering statistics using SPSS. 3rd edition. Sage.
4) Ross, S (2003) An Elementary Introduction to Mathematical Finance, Options and other Topics, CUP
5) Stampfli, J and Goodman, V (2001) The Mathematics of Finance: Modeling and Hedging, Brook/Cole
6) Wilmott, P (2001), Paul Wilmott Introduces Quantitative finance, JohnWiley
7) Etheridge, A (2002), A Course in Financial Calculus, CUP
8) Joshi,M (2003), The Concepts and Practice of Mathematical Finance, CUP.
9) Ruppert, D. (2006) Statistics and Finance: An introduction