Mathematics and Finance MSc

Program Overview

This MSc program in Mathematics and Finance equips students with the knowledge and skills for careers in the financial services sector. It combines core modules in mathematics and finance with optional modules that allow for specialization in areas such as machine learning and algorithmic trading. Through coursework, practical sessions, and a research project, students develop a comprehensive understanding of mathematical principles, financial expertise, and the latest industry trends. Graduates are highly sought after in various roles within the financial industry.

Program Outline

Degree Overview:

This MSc program in Mathematics and Finance is designed to equip students with the knowledge and skills necessary for a successful career in the financial services sector. The program aims to:

• Broaden knowledge of mathematics and quantitative finance: Students will gain a comprehensive understanding of the mathematical principles and tools used in quantitative finance.
• Become familiar with latest industry practice, trends and research: The curriculum incorporates current industry practices, emerging trends, and cutting-edge research in the field.
• Enhance financial expertise: Students will develop their financial expertise through a combination of theoretical learning and practical applications.
• Develop skills for a career in finance and beyond: The program provides students with a strong foundation in quantitative finance, preparing them for a wide range of careers in the financial industry and other related fields.

Outline:

The program is structured around a combination of core modules, optional modules, and a research project.

Core Modules:

• Fundamentals of Option Pricing: This module explores the theory of option pricing, covering its mathematical and conceptual underpinnings.
• Statistical Methods for Finance: Students will gain an understanding of the statistical methods used in finance, including those required by new regulations imposed on banks.
• Quantitative Risk Management: Students will learn about the importance of risk management for financial institutions and the stability of the financial system.
• Interest Rate Models: This module covers the theory and practice of term structure of interest rates, including credit risk, funding liquidity costs, collateral modelling, and multiple curves.
• Computing for Finance: Students will gain proficiency in programming languages like C++ and Python for quantitative finance problems.
• Simulation Methods for Finance: This module examines simulation methods used in finance, including probabilistic numerical methods for PDEs.

Optional Modules:

Students choose five optional modules from a list that includes:

• Algorithmic Trading and Machine Learning: This module focuses on models and techniques used in algorithmic trading, covering both theoretical aspects and practical applications.
• Deep Learning: Students will learn about the structure and components of multi-layer neural nets and develop deep neural networks for financial applications.
• Data Science for Fintech Regtech and Suptech: Methodological Foundations and Key Applications: This module explores the evolution of data science in Fintech, RegTech, and Suptech, applying analytical techniques to real-world challenges.
• Quantum Computing in Finance: Students will discover how quantum computing can be used to solve financial problems, including portfolio optimization and data generation.
• Numerical Methods in Finance: This module covers the main numerical tools used in quantitative finance, including finite difference schemes, Fourier transforms, and linear programming.
• Advances in Machine Learning: Students will deepen their understanding of data analysis and machine learning techniques, with applications in finance.
• Topics in Derivatives Pricing: This module advances knowledge of derivatives pricing and asset allocation, covering stochastic volatility models and jump diffusion models.
• Convex Optimisation: Students will learn how to solve various classes of convex optimization problems, including linear, quadratic, stochastic, conic, and robust programming.
• Advanced Topics in Data Science: Signatures and Rough Path in Machine Learning: Students will explore the properties of rough path analysis and the signature of a path.
• Market Microstructure: This module examines how trades occur in financial markets, covering liquidity measurement, liquidity fragmentation, and dark liquidity.
• Stochastic Control in Finance: Students will apply concepts and techniques from dynamic stochastic optimization and stochastic control theory to quantitative finance.
• Algorithmic and High-Frequency Trading: This module applies probabilistic, variational, and dynamic programming methods to algorithmic and high-frequency trading problems.
• Selected Topics in Quantitative Finance: This module complements existing financial knowledge with a focus on foreign exchange and fixed income markets.
• Portfolio Management: Students will learn about quantitative portfolio management, including factor models and momentum strategies.

Research Project:

The program culminates in a research project, which can be conducted internally or on an external placement with a financial institution. Students will apply their knowledge to solve real-world problems in the financial industry.

Assessment:

The program utilizes a combination of assessment methods, including:

• Coursework: This includes assignments, problem sets, and other assessments related to the core and optional modules.
• Dissertation: The research project culminates in a dissertation, which is a written report detailing the findings and analysis of the project.
• Written examinations: Some modules may be assessed through written examinations.

Teaching:

The program employs a variety of teaching methods, including:

• Lectures: These provide a structured overview of the key concepts and theories covered in each module.
• Tutorials: These provide opportunities for students to engage with the material in a more interactive setting, working through problems and discussing concepts with instructors and peers.
• Practicals: These provide hands-on experience with the tools and techniques covered in the program, allowing students to apply their knowledge to real-world scenarios.
• Independent research project: The research project provides students with the opportunity to conduct independent research under the guidance of a faculty member.

Careers:

Graduates of this program are highly sought after in a range of careers in the financial industry, including:

• Financial Analysts: Analyze financial data and provide investment recommendations.
• Quantitative Analysts: Develop and implement mathematical models for financial applications.
• Risk Management Analysts: Assess and manage financial risks for institutions.
• Trading: Engage in the buying and selling of financial instruments.

Other:

• The program is delivered by the Department of Mathematics at Imperial College London.
• The program is offered on a full-time and part-time basis.
• The minimum entry requirement for the program is a 2:1 degree in mathematics, applied mathematics, statistics, or physics.
• The program is subject to change, and students should consult with the relevant department for the most up-to-date information.

• Full-time: £41,750
• Part-time: £20,875 per year
• Home fee
• Full-time: £41,750
• Part-time
£20,875per year
• Overseas fee
• Full-time: £41,750
• Part-time
£20,875per year
• Your fee is based on the year you enter the university, not your year of study.