Computational Finance

Course detail Description

Computational Finance MSc studies problems of optimal investment, risk management and trade execution from a computational perspective. As with any engineering discipline, computational finance analyses a given problem by first building a model for it and then examining the model.

In computational finance, however, our model is typically analysed by running computer programs, rather than solving mathematical equations. In addition to standard computational methods such as Monte-Carlo option pricing, you will also learn more advanced modelling techniques such as agent-based modelling, in which the model itself takes the form of a computer program.

The course will provide a foundation in the core skills required for successful risk management and optimal investment by giving a grounding in the key quantitative methods used in finance, including computer programming, numerical methods, scientific computing, numerical optimisation and an overview of the financial markets. You can then go on to study more advanced topics, including the market micro- structure of modern electronic exchanges, high-frequency finance, distributed-ledger technology and agent-based modelling.

Teaching

We use lectures, seminars and group tutorials to deliver most of the modules on the course. You will also be expected to undertake a significant amount of independent study.

You are expected to spend approximately 10 hours work per credit for each module you attend in your degree, e.g. 150 hours work for a 15 credit module. These hours cover every aspect of the module.

Assessment methods will depend on the modules selected. The primary methods of assessment for this course are written examinations and coursework. You may also be assessed by class tests, essays, assessment reports and oral presentations.

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Structure

Year 1

Courses are divided into modules, and students on this course take modules totalling 180 credits.

You are required to take:

• Individual Project (60 credits)
• Scientific Computing for Finance (15 credits)
• Quantitative Methods in Finance (15 credits)
• Industry-Expert Lectures in Finance (15 credits)
• Agent Based Modelling in Finance (15 credits)
• High-Frequency Finance (15 credits)

In addition, you are required to take 45 credits from a range of optional modules that may typically include:

• Financial Markets (15 credits)
• Fundamentals of Digital Signal Processing (15 credits)
• Cryptography (15 credits)*
• C++ for Financial Mathematics (15 credits)
• Nature-Inspired Learning Algorithms (15 credits)*
• Statistics in Finance (15 credits)
• Incomplete Markets (15 credits)
• Software Engineering & Underlying Technology for Financial Systems (15 credits)
• Distributed Ledgers & Crypto-Currencies (15 credits)
• Security Management (15 credits)

You can also choose level 6 module:

• Machine Learning (15 credits)*

* Cannot be taken by students who have previously studied in the Department of Informatics and have already taken this module at Level 6.

King’s College London reviews the modules offered on a regular basis to provide up-to-date, innovative and relevant programmes of study. Therefore, modules offered may change. We suggest that you keep an eye on the course finder on our website for updates.