MSc in Statistics with Applications to Finance A taught
masters course in statistics combining in-depth training in mainstream
advanced statistical modelling with a specialisation in financial
mathematics.
Commencing: September 2011
How to Apply:You can apply online by visiting
http://www.leeds.ac.uk/students/apply/htm, where you can also
download a form for completion by hand.
Want to know more? Continue reading this page, or
contact
Ms Iwona Malinowska
Taught Postgraduate Administrator
School of Mathematics
Telephone: + 44(0) 113 343 5111
email: maths-msc@maths.leeds.ac.uk
Contact a lecturer:
Dr Stuart Barber
email: stuart@maths.leeds.ac.uk
Telephone: +44 (0) 113 343 5146
Course overview
The MSc in Statistics with Applications to Finance at the
University of Leeds is a focussed degree programme
enabling students from a wide range of backgrounds to both
broaden and deepen their understanding of statistics and
financial applications.
The programme provides training in a core of statistical
techniques (and transferable skills) suitable for either
careers in statistical finance or for further academic research.
The course is a full-time MSc lasting 12 months, starting in
September. The Diploma is just over 8 months duration. The Course
consists of two semesters of taught modules, with the third semester
(MSc only) devoted to a major dissertation. Within each semester
there are three compulsory modules. However, students tailor the
course to meet their individual needs through selection of a further
module in each semester from a variety of options.
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Why study for an MSc?
There is a shortage of well-qualified statisticians in the
UK and other countries. Numeracy, in general, is an
attribute keenly sought after by employers.
The emergence of data mining and analysis means that
demand for statisticians is growing across a wide range of
professions, in particular in actuarial companies, betting
and gaming industries, and other financial organisations.
The course is designed specifically to meet this
demand.
Many statistical careers require people educated to
masters degree level. This course is designed to build on
existing mathematical skills and deepen knowledge of
statistics in order for you to access a variety of
professions or pursue further research as a PhD student.
Why study Statistics at the University of
Leeds?
Simply because you will be taught by leading figures in
their field of specialisation! As well as the expertise
within the Statistics Department, the MSc also draw on the
experience of the centre in Financial Mathematics, a joint
venture between the School of Mathematics and Leeds
University Business School headed by Professor Klaus Schenk-Hoppé
Our School’s reputation is recognised worldwide through
research collaborations with other universities, with
government bodies, sectors of industry, hospitals, and
through a variety of interdisciplinary projects with other
departments within the University.
Our Department is home to LASR - Leeds Annual Statistics
Research workshop - an event originated (and still led) by
Professor Kanti Mardia over 25 years ago. It has grown
from a small group of specialists sharing common interests
in development of their research into an international
platform attracting experts at the cutting-edge of
statistical research.
Other areas of research include:
- Modern Data Analysis including robustness (a key concept
in modern statistical modelling); pattern recognition and
machine learning; statistical computation and wavelets.
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Statistical Bioinformatics including phylogeny; sequential
analysis; structure analysis and gene expression analysis.
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Probability and Stochastic Processes comprising parameter
estimation for Markov processes; stochastic financial
models; stochastic differential equations; propagation and
extinction in catalytic media; intermittency in random
media; limit theorems for sums and extreme values and
probabilistic methods in number theory and combinatorics.
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Course details
Semester One
Compulsory modules: - Statistical Computing
An
introduction to methods of statistical computing. Essential for the
applied statistician, with an emphasis on sampling-based methods, such
as Markov chain Monte Carlo.
- Stochastic Financial Modelling
Financial investments
such as stocks and shares are risky: their value can go down as well
as up. To compensate for the risk in a fair market, a discount is
needed. This module will develop the necessary probabilistic tools to
enable investors to value such assets.
- Discrete Time Finance
This module develops a general
methodology for the pricing of financial assets in risky financial
markets based on discrete time models.
Optional Modules
- Statistics and DNA
Modern biological experiments produce large data sets involving
information related to DNA. This module gives the basic biological
background before looking at a range of data types and methods to
analyse them. Among others, we will look at topics in evolution,
genetics, and forensic science.
- Robust Regression and Smoothing
A fundamental
statistical tool is the simple linear regression model, which predicts
the value of a normally-distributed response variable from a predictor
variable. This module explores many ways to extend this simple model
to cope with non-linear relationships and data corrupted by non-normal
errors
Semester Two
Compulsory modules:
- Time Series and Spectral Analysis
In time series,
measurements are made at a succession of times, and it is the
dependence between measurements taken at different times which is
important. We concentrate on techniques for model identification,
parameter estimation and forecasting future values of the time
series.
- Continuous Time Finance
Continuous time models play a
central role in pricing of financial assets under more challenging
circumstances than can be handled with discrete time models.
- Risk Management
This module gives comprehensive
coverage of mathematical and practical approaches to financial risk
management. Avoiding the disastrous consequences of badly managed risk
requires detailed mathematical knowledge of how to quantify financial
risk and stress-test a hedge.
Optional modules:
- Independent Learning Skills
An introduction to
research methods including literature search, writing styles,
mathematical typesetting and programming skills.
- Generalised Linear Models and Survival Analysis
The usual linear regression model deals well with normally distributed
data, but what about data where the response is a categorical
variable? We see how to cope with binomial and Poisson distributions
as part of a wider regression framework, the generalised linear model,
and also how to adapt to the reliability or survival data common in
medical and actuarial settings.
- Statistical Theory
We often use statistical tests and
estimators without fully exploring the theoretical basis for their
use. Here, we look more deeply into the mathematics behind statistical
inference and compare the two main approaches to statistics:
Frequentist and Bayesian inference.
Semester Three
The taught course is primarily assessed by end-of-semester
examinations with a small component of continuous assessment. The
Semester Three project is assessed by a written dissertation and
short oral presentation.
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