Tailor your learning with electives

Electives allow you to personalise your programme to match your own interests. You will take either three (if you do the Research Project) or four (if you do the Applied Project) from the following elective modules. All students will be required to take at least one key elective.

Administrative label

Key electives

Advanced Options Theory

If you aspire to be a quantitative analyst in the equity derivative area, this module is a must. It will challenge you to expand your knowledge beyond the Black-Scholes model and apply quantitative tools to the pricing of exotic options. The module will introduce some of the more technical and theoretical aspects of option pricing.

Asset Allocation & Investment Strategies

This is an advanced elective in investments and portfolio management. You will discuss the key trading strategies used by hedge funds and demystify the secret world of active investing. The module combines the latest research with real-world examples and explores several different strategies in depth, including fundamental tools for investment management, dynamic portfolio choice, equity strategies, macro strategies, yield curve logic and arbitrage strategies.

Computational Finance with C++

This elective takes the key mathematical models in finance and develops the numerical methods used to solve them using C++. The numerical methods you will study are: Monte Carlo simulation, binomial trees, finite differences, convey optimisation and eigenvalue methods. You will learn about the financial models and discuss how to effectively design a numerical method using object oriented techniques.

You will have the opportunity to directly work on practical examples requiring hands-on interaction with the programming language.

Credit Risk

This module provides you with a broad perspective of credit risk. You will study how to assess credit risk associated with individual exposures, and discuss major literature in the field and some related applications. The module also covers aspects of univariate or single-exposure risk and investigates the pricing of defaultable bonds and single named credit derivatives.

Fixed Income Securities

Fixed income securities make up a very substantial proportion of all investments and financing strategies in today’s financial markets. The need to price and hedge this array of products accurately has led to a prolific literature in the area. The module covers the main continuous-time term structure models and valuation techniques.


This module provides an introduction to the main issues involved in insurance markets. Issues covered include the drivers of demand and supply for general insurance and life insurance, the limits of insurability and how scoring and auditing can help insurers to fight claims fraud.

Other electives

Advanced Financial Statistics

This module aims to provide students with more advanced tools of time series and econometrics than the Financial Statistics module. Applications to asset pricing and risk management will also be covered.

Applied Trading Strategies

This module provides insight into financial trading strategies from an industry practitioner’s perspective. The module covers the wide spectrum of strategies across asset classes and hedge fund styles with an emphasis on investment /arbitrage opportunity and risk management. The module also includes quantitative pricing models with backtesting in Python across different market regimes. The module aims to study trading strategies in a non-technical intuitive manner using a “first principles” approach.

Banks, Regulation and Monetary Policy

In this elective you will analyse banks’ main risks and activities on both their assets and liabilities, including off-balance sheet risks and financial globalisation, with special emphasis on the effects and implications of bank regulation and monetary policy. You will also study issues such as the determinants and consequences of financial crises and come to understand interactions between financial globalisation and banks.

Big Data in Finance I

Over the past few years, there has been an explosion of interest in the use of large datasets and new empirical techniques to make financial decisions of all kinds. In this elective we examine how the combination of large datasets, empirical techniques including machine learning, and insights from behavioural finance are helping in making more efficient financial decisions. Two areas in which progress has been especially rapid are credit analytics (predicting default in personal loans, mortgages, and firms), and asset management. This elective focuses on these specific markets, considering them from supply, demand, and regulatory perspectives. You will build empirical models to illustrate important concepts throughout the elective.

Big Data in Finance II

Big Data in Finance II builds on and complements insights from Big Data in Finance I. The module will focus on three key techniques in Big Data analysis and machine learning, and their applications to finance. First, we explore unsupervised machine learning models (e.g. clustering algorithms) and their applications to recommendation algorithms in finance. Second, expanding the introductory material on neural networks in Big Data in Finance I, the module will develop this material further to cover Deep Learning techniques, which will then, as in Big Data in Finance I, be applied to credit scoring and/or portfolio choice problems. Third, the module will introduce and discuss reinforcement learning models, with potential applications to portfolio selection and trading strategies.

The aim of this module is to introduce tools used to tackle problems related to panel data analysis in empirical finance and economics. Using applications from economics and finance, you will learn to understand and critique research designs and causal claims. The goal is to sharpen your skills as consumers and producers of empirical research in empirical finance. You will learn how to interpret coefficients, how to identify causal mechanism, and how to test for robustness and sensitivity.

Enterprise Risk Management

This module introduces modern methods of enterprise risk management applicable for financial organisations, including insurance companies and pension funds. It analyses different types of risk and methods for measuring and managing them before examining how insurance companies and pension funds implement risk mitigation techniques.

International Finance

Foreign exchange (FX) is not only the most heavily traded of all financial assets, it has the clearest interface between macroeconomics and finance. This module will introduce you to the main theoretical models used to understand FX markets as well as in-depth analysis of their work.

Introduction to Quantitative Investing (international elective)

This elective offers an introduction to analytical techniques and quantitative methods relevant for algorithmic trading. Topics will include the basics of automated execution, pairs trading and long-short equity trading strategies. The module is taught in two parts with the first part delivered online and the second part is an international study trip. Traditional lectures will be complemented by guest speakers, company visits and experiential learning activities. The class of 2018-19 traveled to New York for an intensive study experience - read more.

There will be an additional cost for taking this elective, which is reviewed on an annual basis.

Macroeconomics and Finance for Practitioners (international elective)

This module allows you to experience finance in a different economy. The module is taught in two parts with the first part delivered online and the second part is an international study trip. Traditional lectures will be complemented by guest speakers, company visits and experiential learning activities. The class of 2018-19 travelled to Dubai for an intensive study experience in the capital of an emerging economy - read more.

There will be an additional cost for taking this elective, which is reviewed on an annual basis.

Machine and Deep Learning with Finance Application

This module will introduce you to big data analysis using machine learning techniques. You will utilise machine learning methods to use computational textual analysis and empirical modelling to quantify trends and sentiment in big data.

Private Equity and Venture Capital

This elective allows you to apply key principles of private equity and venture capital to the financing of leveraged buyouts and early-stage ventures. The elective teaches students how to apply what they have learned in class to real life work situations by inviting inspiring speakers to present on campus throughout the module. The guest speakers come from various areas in the industry and discuss how they make transactions in real-life and what their roles entail on a daily basis.

Structured Credit and Equity Products

This module provides an in-depth analysis of credit and equity derivative products. We focus on corporate derivatives and cover the most important products, which serve as building blocks for structuring customised and sophisticated products.

Topics in Corporate Finance

This module covers a variety of topics in corporate finance, with the focus on optimal corporate financial policy. You will learn what factors determine the “optimal” capital structure and how the interplay of those factors can affect financing decisions in a way that creates value on the right hand side of the balance sheet.

Topics in Fintech Innovation

This elective offers a series of topics on fintech innovation including block chain and its applications, digital payments and financial inclusion, and technology and infrastructure solutions.

Wealth Management and Alternative Investments

This module aims to introduce you to areas of financial planning that are more specific to private wealth management. It will introduce you to the types of client, and their respective investment needs and look at issues such as succession planning and multi-jurisdiction tax planning. Finally it examines the role of alternative investments (hedge funds, real estate and private equity) in building a diversified investment strategy.

Electives available and course outlines are subject to change. Imperial College Business School reserves the right to alter courses whenever they need to be amended or improved. Faculty may also change as and when required.

Studying at MSc Risk Management & Financial Engieering has been a fantastic experience. The programme integrated mathematics, finance and computer science and goes into great depth. It is taught by great faculty and industry practitioners. We have gained not only the quantitative skills, but also the insight in the industry development.
Qianni Zhang
MSc Risk Management & Financial Engineering 2017