This module offers you a standard introduction of the field of corporate finance at postgraduate level. You consider the classical areas of Modigliani-Miller irrelevance, Taxes and capital structure, Trade-off theory and Pecking order theory of capital structure, before exploring the more modern areas, which are essentially based on contract theory.

Master the pricing of financial derivatives and their use for hedging financial risks. You study the basics of futures and options, analyse the Black-Scholes and binomial option pricing models, and consider various numerical techniques for pricing financial derivatives. Futures and options are then utilised in the context of hedging financial risks, and you are introduced to the concept of volatility trading and the treatment of volatility as an asset class.

Gain a formal introduction to asset pricing theories and empirical findings. You review the fundamental theories of the expected utility, asset pricing kernels, and risk-neutral valuation, covering the Capital Asset Pricing Model (CAPM), and linear factor models arising from the Arbitrage Pricing Theory (APT). You also discuss empirical asset pricing studies.

Understand the process of portfolio management. You cover the main concepts such as efficient diversification, managing risk exposures, and the valuation of financial assets that are at the core of managing investment portfolios, and pay special attention to the practicalities of the implementation of these concepts.

Consider the use of modern econometric techniques in the analysis of financial time series. You cover multivariate models for stationary and non-stationary processes, such as Vector Autoregressive models, consider appropriate models for volatility, and study Markov processes and simulation methods used for financial modelling.

Behavioural finance rejects crucial tenets of mainstream finance such as the Efficient Market Hypothesis (EMH) on the basis that agents are less than fully rational, and that arbitrage fails to eliminate mispricing. Instead it identifies market anomalies or regularities such as holiday effects that are at odds with the EMH. You learn to use ideas from cognitive psychology, such as overconfidence, and aspects of imperfect arbitrage to explain these.

The recent financial crisis and credit crunch have demonstrated that risk management was too narrowly defined. In this course you examine the Value at Risk (VAR) measure of financial risk developed in the 1990s, before discussing the new post-crisis Regulatory environment.

Discover the concepts and tools that are useful to asset managers who want to use fixed income securities for investing, market-making or speculating. You first study fixed income markets and instruments, before going on to explore basic concepts of bond portfolio management and investigating the quantitative tools used to value bonds and manage bonds' portfolios.

This module provides you with an understanding of non-time-series data analytic approaches in finance. It covers methods for cross-sectional, panel and qualitative analysis and their applications whereby all topics are illustrated with relevant examples. Cross-sectional data are organised over individual groups (eg households, firms or countries) and have no time dimension. They may include discontinuous data (eg binary), qualitative or categorical data and are essentially non-numerical. Examples include: survey responses, textual analysis of social media or interviews. Panel data or longitudinal data are multi-dimensional data streams involving measurements over time. As such, panel data consists of researcher's observations of numerous phenomena that were collected over several time periods for the same group of units or entities. For example, a panel data set may be one that follows a given sample of individuals over time and records observations or information on each individual in the sample. The nature and advantages of panel data has led to numerous applications in finance and economics research.

Financial technology (FinTech) is rapidly changing the financial services industries. The module provides an introduction to FinTech by analysing its impact on major banking and financial activities. Specifically, the module illustrates the effect of FinTech on lending activities (P2P lending, crowdfunding) and credit evaluation tools (big data, machine learning and artificial intelligence tools), investment activities (trading and robo-advising), payments (crypto currencies and new payment tools), insurance (InsureTech). In final, the module analyses the topic of ethics and regulation of FinTech.

This course will provide you with a thorough grounding in both the theory and practice of time series econometrics with applications to topics relevant to finance. This knowledge, along with training in industry standard computing software, will be highly beneficial to students undertaking further study or wishing to work in the financial sector.

This module covers topics in mathematics that are required in Masters-level finance courses at the University of Essex. You focus on the basics of linear algebra, differential calculus including optimisation and dynamics.

Explore the basics of the structure and environment of banking, and selected aspects of the applied economics of the modern banking firm. You study structure-conduct-performance, competition, bank efficiency, regulation, international banking and bank failures and crises.

Investment banks are major engines of most economies today. This module will describe the operations of primary global financial and advisory markets and the key players in the investment and corporate banking fields. The module will also provide an overview of the corporate and investment banking industry, with a focus on the main activities of full-service investment banks, including M&A advisories and the raising and underwriting of financing deals.