This module equips you with the latest skills in data science, financial econometrics and quantitative finance to analyse and model asset price dynamics using techniques at the research frontier in these areas, including the work with high-frequency data, relevant for the job market (especially in Quants) and the MSc dissertation streams.

It will teach you important features of financial time series, key modelling approaches in the field, how to use high-frequency data to construct the latest volatility estimators and the most appropriate methods for forecasting price volatility and risk. It will also give you practical experience of analysing market prices, constructing volatility estimators and designing forecasting analyses through empirical projects implemented with MatLab.

Providing you with a strong foundation for understanding both the economics of financial markets and the main types of securities traded in these markets, this module focuses on bonds, futures, swaps and options. It strikes a balance between the theory and practice as well as making important links between models and the real world. The emphasis is on both principles and problem solving.

Topics covered include bonds, the economics of derivatives markets, futures and forwards, swaps, and options.

This module contributes to the following CFA syllabus areas:

• Securities Markets (CFA level I)
• Derivative Investments (CFA levels I and II)
• Debt Investments (CFA levels I, II and III)

The aim of the module is to provide a thorough understanding of the issues that are relevant to the asset management industry with special emphasis on understanding and measuring investment performance.

Our discussions will focus on mutual funds, hedge funds and exchange-traded funds with less emphasis given to other fund management vehicles. The course will not cover security analysis.

This module offers an in-depth exploration of the mathematical and algorithmic foundations of artificial intelligence, with a specific focus on machine learning. You will learn how to analyse real-world challenges, design appropriate AI models, and apply machine learning algorithms to complex datasets. Key algorithms will be discussed in detail, including their motivation, underlying theory, implementation, and practical applications using programming and statistical software.

By the end of the module, you will have the skills to model real-world phenomena, assess the effectiveness of various machine learning algorithms, and make evidence-based decisions grounded in statistical learning theory.

This module will enable you to understand the key concepts and methods in data science, econometrics, and quantitative finance to carry out independent empirical work required for the job market, more advanced modules in accounting and finance and the MSc dissertation streams.

The module aims to provide you with an understanding of crucial advancements in artificial intelligence (AI), its application in corporate settings, and a comprehensive overview of cyber security within a digitised business landscape. It provides an overview of AI and discusses its current trends and future implications. Several case studies of the application of AI in finance, marketing, and customer service link the learning to real-world experiences.

The module further discusses the ethical considerations of using AI for business, including privacy concerns, copyright issues, and potential biases. It then builds a strong foundation in understanding the basics of cyber security, including types of cyber threats and the importance of cyber hygiene. It discusses strategies for safeguarding businesses and their digital assets, including encryption, firewalls, and intrusion detection systems. It introduces best practices for developing resilient cyber security systems, as well as familiarises students with risk management principles and compliance with regulatory requirements related to data protection and privacy.

Turbulent decades of economic crises and increased volatilities across all asset classes have brought about innovative and strategic derivatives solutions to manage financial risk and create value. Their failure has typically been due to a lack of understanding of how to use and price derivatives. Understanding derivatives’ dynamics, risks, valuations and uses has become more important than ever.

Although there are many derivatives structures, the key to understanding derivatives is that all financial products, no matter how complex, are portfolios of just two fundamental building blocks: a swap (forward) and an option. This module provides this understanding in a rigorous, consistent and coherent framework.

Stochastic calculus is a theory that enables the calculation of integrals with respect to stochastic processes. This module begins with the study of discrete-time stochastic processes, defining key concepts such as martingales and stopping times. This then leads on to the exploration of continuous-time processes, in particular, Brownian motion.

Students will learn to derive basic properties of Brownian motion and explore integration with respect to it. They will also examine the derivation of Ito's formula and how to apply this to Brownian motion.

Over the course of the module, students will also learn to justify and critique the use of stochastic models for real-life applications, and to use the stochastic calculus framework to formulate and solve problems involving uncertainty – a skill that underpins financial mathematics.