ME200: Computational Methods in Financial Mathematics

The financial industry increasingly relies on large volumes of numerical data as financial products become more complex. As a result, analysts and financial engineers have turned to computational methods and numerical analysis to make sense of the financial data available to them to make investment decisions and manage financial risk.

In this hands-on course, you will be introduced to the models and theory necessary to develop your computational skills in the field of financial mathematics. Covering topics such as the Monte Carlo method, stochastic models, the binomial tree model, the theory of risk-neutral pricing, derivative pricing and the interpretation of random variables, you will learn how computational methods can be used to evaluate different financial scenarios.

During supervised programming sessions, which include an introduction to programming in Python, you will have the opportunity to implement the computational methods introduced to you using relevant examples. By the end of the course, you will be able to apply these methods to new numerical experiments based on real-world cases within the financial industry, including pricing derivatives, measuring risk, and designing an investment strategy. 

Prerequisites: Calculus at lower undergraduate level and an introductory course in probability or statistics.

Level: 200 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: Two written examinations

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

This course is suitable if you want to have a grounding in the growing field of computational methods within financial mathematics. You are not expected to have prior programming experience and will be taught how to programme in Python.

The course is largely self-contained and reviews the necessary mathematical concepts used during the course. It is especially useful if you are targeting a role in private equity, finance, banking or consulting.

• Understand the mathematical foundations of computational methods
• Analyse and price financial derivatives numerically using the programming skills taught in class
• Discuss the use Students are provided with both the mathematical foundations and the programming skills to analyse and price financial derivatives numerically.
• Understand how to generate random numbers and how they are used in computational methods
• Apply the Monte Carlo method using Python
• Discuss the concepts of non-arbitrage and replication in the one-period binomial model

The design of this course is guided by LSE faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.