入学要求 Requirement：
学术要求：BSc (or equivalent) in a subject containing a substantial mathematical or statistical component, usually at level 2.1 or above (or equivalent).
英语要求： IELTS：A minimum IELTS score of 6.0 with not less than 5.5 in listening and reading, and not less than 5.0 in speaking and writing.
学费 Tuition Fee：2011/2012 ￡13,700
课程特征 Course Features：
The MSc in Statistics with Applications to Fiance 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.
课程内容 Course Content ：
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
•Project in Statistics
A three-month research project undertaken in the Summer on a topic (chosen in conjunction with project supervisors) culminating in a dissertation on that project.
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.