MSc Actuarial Science

This class will provide an introduction to financial decision-making, and much of the relevant analysis will be developed from the standpoint of corporate finance. It'll explain how a company should decide on the investments to be undertaken to meet its objectives, generally assumed to be the maximisation of its value. It will be demonstrated that this will require a rate of return on its investments in excess of the return available in the capital market on equally risky financial investments. As a result, it will be necessary to develop an understanding of the capital market risk-return relationship. This will require an appreciation of the nature of risk and how this can be managed by the development of portfolios.

Even though the focus of the class will be on corporate finance, it'll also require an appreciation of how the risk-return tradeoff is determined in the capital market.

Find out more in the class outline for Principles of Finance.

The course and thus this introductory class is aimed at graduates who have not previously studied statistics at university level. The class will provide the foundation elements of probability and statistics that are required for the more advanced classes studied later on.

The aim of this class is to equip you with the essential technical knowledge in probability and statistics required for the Semester 2 classes which then apply these ideas.

You’ll learn about key macroeconomic variables and how changes in GDP, unemployment and inflation can impact on day-to-day policy and business decisions. You'll also study objectives and challenges in the design of macroeconomic policies by central banks and government’s finance departments and gain an understanding of how policies impact on the macroeconomic environment.

10 credits

This class will introduce you to some key concepts and principles of economics, focusing particularly on consumer theory and the theory of the firm. You’ll be shown how these concepts and theories are applied to the analysis of a range of contemporary real world issues.

10 credits

The aim of the class is to provide you with an understanding of the main ideas of behavioural finance. A particular emphasis is placed on understanding the roles of non-rational actions and the development of new financial models that incorporate these ideas.

You'll engage with up to date research and develop a critical view of existing and new finance theories and models. It aims to introduce you to the rapidly evolving area of behavioural finance. You'll develop an understanding of behavioural finance and an appreciation of its possible implications and applications.

This class introduces you to behavioural finance and provides you with an understanding of the main flaws of 'traditional' finance theory from a behavioural finance viewpoint. It will allow you to develop the ability to discuss issues arising from violations of the rationality assumption and will enable you to evaluate new theoretical models based on research in psychology.

The course will allow you to appreciate the role of new developments in finance and their possible implications for established views of the functioning of financial markets.

Find out more in the class outline for Behavioural Finance.

The aim of this class is to develop an appreciation of the investment characteristics of different types of securities, particularly bonds and shares, and to develop an understanding of how such securities are valued. This class will build on the analysis developed in the first semester class, Principles of Finance. It will consider the determination of interest rates, the valuation of bonds, the management of bond portfolios, and the valuation of equities.

Find out more in the class outline for Security Analysis.

The aim of this class is to examine the Markowitz (1952) approach to optimal portfolio selection. The class explores issues relating to optimal portfolio choice and issues in passive and active fund management through the lens of the nature of variance, covariance, risk and return. The class introduces practical applications and an extension of basic theory.

Find out more in the class outline for Portfolio Theory & Management.

This is a core class for the MSc in International Banking & Finance. The aim is to develop further the analysis of risk introduced in the core finance class and to introduce you to some additional risk management techniques employed in practice.

It provides an overall view of risk management, but will focus on the application of the analysis within the context of the banking sector. The class employs some of the basic principles of financial analysis to consider the application of risk analysis. It explores issues relating to risk management in the banking sector, with a particular focus on the regulatory requirements stemming from the Basel Accord.

Find out more in the class outline for Risk Management for Banks.

You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.

The class aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.

This module will explore the entire process of structuring a risk problem, from modelling it to communicating recommendations, both theoretically and in practice.

Risk management is linked with decision analysis in so far as we explore decision making under uncertainty and it has links with quantitative business analysis as we explore the use of statistics in understanding risk. However, the topic has some unique attributes such as risk communication and the role that experts play in risk assessment.

This class provides an introduction to the theory and application of spatial statistics. It aims to develop an understanding of statistical methods for visualising and analysing different types of spatial data, in particular Bayesian approaches for constructing more sophisticated models. The class emphasises practical inference and application and introduces the most commonly used software tools for spatial statistical analysis.