Financial Mathematics MSc

Program Overview

This MSc program in Financial Mathematics provides students with a comprehensive understanding of quantitative finance. It covers general finance theory, finance models, and programming, equipping graduates with the knowledge and skills for careers in financial software development, actuarial science, investment banking, and quantitative analysis.

Program Outline

Degree Overview:

This MSc program in Financial Mathematics is designed for graduates with a background in science, engineering, and mathematics. It aims to provide a comprehensive understanding of quantitative finance, encompassing general finance theory, finance models, and programming. The program leverages Sussex's strong interdisciplinary research foundation, with instruction delivered by experts from both the Department of Mathematics and the University of Sussex Business School.

Objectives:

The program aims to equip students with the knowledge and skills necessary to succeed in the fast-paced world of finance. Upon completion, graduates will be able to:

• Understand the main aspects of quantitative finance, including general finance theory, finance models, and programming.
• Apply their knowledge to real-world financial problems.
• Develop strong analytical and problem-solving skills.
• Communicate their findings effectively.

Outline:

Duration:

1 year full-time

Start Date:

September 2024

Modules:

Core Modules:

• Dissertation (Financial Mathematics): This module allows students to conduct independent research on a topic of their choice within the field of financial mathematics.
• Corporate Finance: This module covers the fundamental principles of corporate finance, including capital budgeting, valuation, and risk management.
• Financial Mathematics (L.7): This module provides a rigorous introduction to the mathematical foundations of finance, including stochastic calculus, option pricing, and risk management.
• Financial and Time Series Econometrics: This module explores the use of econometric methods for analyzing financial data, including time series analysis and forecasting.
• Financial Portfolio Analysis: This module examines the principles of portfolio optimization and risk management, including asset allocation and performance evaluation.
• Mathematical Models in Finance and Industry: This module introduces students to the application of mathematical models in various financial and industrial settings.

Optional Modules:

• Advanced Numerical Analysis (L.7): This module covers advanced numerical methods for solving mathematical problems, including optimization, interpolation, and integration.
• Linear Statistical Models (L7): This module explores the theory and application of linear statistical models, including regression analysis and ANOVA.
• Partial Differential Equations: This module introduces students to the theory and solution methods for partial differential equations, which are widely used in finance.
• Programming through Python: This module teaches students how to program in Python, a popular language for data analysis and finance.
• Dynamical Systems: This module explores the theory and application of dynamical systems, which are used to model complex systems in finance.
• Financial Invest & Corp Risk Analysis: This module focuses on the analysis of financial investments and corporate risk management.
• Machine Learning and Statistics for Health (L7): This module introduces students to the use of machine learning and statistical methods in healthcare.
• Monte Carlo Simulations (L7): This module covers the theory and application of Monte Carlo simulations, which are used to estimate the value of financial instruments.
• Numerical Solution of Partial Differential Equations (L.7): This module explores numerical methods for solving partial differential equations, including finite difference methods and finite element methods.
• Random processes (L.7): This module provides a rigorous introduction to the theory of random processes, which are used to model financial markets.

Teaching:

The program is taught by a team of experienced academics from the Department of Mathematics and the University of Sussex Business School. The teaching methods include:

• Lectures
• Seminars
• Tutorials
• Group work
• Individual projects

Careers:

Graduates of this program are well-prepared for a variety of careers in the financial industry, including:

• Financial software developer
• Actuary
• Investment banker
• Quantitative analyst

Other:

The program is designed to provide students with a strong foundation in quantitative finance, which will enable them to pursue a successful career in the financial industry. The program is also highly relevant to other industries that require strong analytical and problem-solving skills, such as consulting, research, and data science. The University of Sussex will notify students of any material changes to the program at the earliest opportunity.