Statistics with Applications to Finance MSc

Program Overview

This MSc program in Statistics with Applications to Finance combines advanced statistical modeling with financial mathematics, equipping students for careers in the financial sector. The program offers a range of compulsory and optional modules, including coursework in risk management, time series analysis, and specialized topics. Through a dissertation project, students gain practical experience and prepare for roles in actuarial science, investment analysis, and risk assessment.

Program Outline

Degree Overview:

This MSc program in Statistics with Applications to Finance is designed to provide students with a comprehensive understanding of statistics and its applications in the financial world.

• Combine in-depth training in advanced statistical modeling with specialization in financial mathematics.
• Develop expertise in areas such as probability, financial mathematics, modern applied statistics, and analysis.
• Gain practical experience through a dissertation project focusing on a chosen topic.
• Prepare students for careers in various job roles within the financial industry.

Outline:

Course Structure:

• The program is delivered over 12 months full-time.
• The first two semesters consist of taught modules.
• The third semester is dedicated to a major dissertation.
• Students take compulsory modules in each semester, with optional modules available to tailor the program to individual interests.

Year 1 Compulsory Modules:

• Discrete Time Finance (15 credits): This module introduces students to the fundamentals of financial modeling in discrete time, covering topics such as asset pricing, portfolio optimization, and risk management.
• Continuous Time Finance (15 credits): This module builds upon the concepts introduced in Discrete Time Finance, exploring financial modeling in continuous time, including stochastic calculus, option pricing, and derivative pricing.
• Risk Management (15 credits): This module provides a comprehensive understanding of financial risk management, covering topics such as risk identification, measurement, and mitigation.
• Time Series and Spectral Analysis (15 credits): This module focuses on the analysis of time series data, covering techniques such as forecasting, smoothing, and spectral analysis.
• Dissertation in Statistics (60 credits): This module involves a three-month research project undertaken in the summer, culminating in a dissertation on the chosen topic.

Year 1 Optional Modules (Selection of Typical Options):

• Mixed Models (10 credits): This module explores the use of mixed models in statistical analysis, covering topics such as random effects, fixed effects, and model selection.
• Bayesian Statistics (10 credits): This module introduces students to Bayesian statistical methods, covering topics such as prior distributions, posterior distributions, and Bayesian inference.
• Generalized Linear Models (10 credits): This module focuses on the application of generalized linear models in statistical analysis, covering topics such as logistic regression, Poisson regression, and negative binomial regression.
• Mixed Models with Medical Applications (15 credits): This module explores the application of mixed models in medical research, covering topics such as longitudinal data analysis, clinical trials, and survival analysis.
• Introduction to Programming (5 credits): This module provides an introduction to programming concepts and techniques, focusing on languages commonly used in statistical analysis.
• Computations in Finance (15 credits): This module explores the use of computational methods in finance, covering topics such as numerical optimization, Monte Carlo simulation, and financial modeling.
• Linear Regression, Robustness and Smoothing (20 credits): This module covers linear regression models, including robust methods and smoothing techniques.
• Multivariate and Cluster Analysis (15 credits): This module explores multivariate statistical methods, including principal component analysis, factor analysis, and cluster analysis.
• Bayesian Statistics and Causality (15 credits): This module focuses on the application of Bayesian statistics to causal inference, covering topics such as causal graphs, Bayesian networks, and causal mediation analysis.
• Generalised Linear and Additive Models (15 credits): This module explores the use of generalized linear models and additive models in statistical analysis, covering topics such as generalized additive models, smoothing splines, and penalized regression.
• Independent Learning and Skills Project (15 credits): This module provides students with the opportunity to undertake an independent research project, developing their research skills and knowledge.
• Statistical Computing (15 credits): This module focuses on the use of statistical software packages, covering topics such as data manipulation, statistical analysis, and visualization.

Assessment:

• The taught modules are primarily assessed through end-of-semester examinations, with a small component of continuous assessment.
• The dissertation project is assessed through a written dissertation and a short oral presentation.

Teaching:

• The program is taught jointly by the School of Mathematics and the Leeds University Business School.
• Students benefit from a combination of lectures and small group workshops delivered by specialists in each school.
• The program utilizes extensive IT facilities and a wide range of materials to enhance learning.
• The program team includes academics specializing in various areas of mathematics and statistics, as well as industry professionals and postgraduate researchers.

Careers:

• There is a global shortage of well-qualified statisticians.
• The demand for statisticians is growing across various sectors, including actuarial, betting and gaming, charitable organizations, commercial, environmental, and financial organizations, forensic and police investigation, government departments, market research, medical and pharmaceutical organizations.
• The program is designed to meet this demand and prepare students for a variety of professions.
• Many statistical careers require a Masters degree level of education.
• The program builds upon existing mathematical skills and deepens knowledge of statistics to enable students to pursue further research as PhD students.

Other:

• The program is accredited by the Royal Statistical Society.
• The School of Mathematics and Leeds University Business School have strong links with industry and research institutions.
• Students have access to excellent teaching facilities and computing equipment.
• The program offers a diverse and supportive community of mathematicians from around the world.
• The University of Leeds is ranked 82nd in the world according to the QS World University Rankings 2025.

UK fees: £13,750 (Total)

International fees: £29,750 (Total)