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.

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.

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.

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.

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.

The module introduces students to financial markets as well as providing a detailed introduction to the quantitative methods that are a pre-requisite to other CCFEA modules. Students will be introduced to financial markets such as equities, bonds, interest rates, forwards, futures and foreign exchange. Applications of calculus and statistical methods to finance are also presented.

The modules introduces students to computational thinking in economics and finance by looking at different relevant models and theories, such as agent-based modelling and game theory. Students will also be introduced to various applications, such as financial forecasting, automated bargaining and mechanism design.

This module is a mix of theory and practice with big data cases in finance. Algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structures and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.

How do individuals make decisions? When does classic economic theory not predict empirically observed behaviour? And how do you then use behavioural economics to reconcile your empirical findings with theoretical models? Learn about empirical and theoretical research in behavioural economics that can be used to explain individual decision making.

This module will train you in R and Python programming alongside applications to agent-based computational economics models and machine learning. You don't need prior programming experience. You'll gain hands-on experience in laboratory sessions and equip yourself with computational techniques that can be applied to solving real-world economic and financial problems based on large-scale data.

This postgraduate module equips you with the key tools in modern data science, with a focus on machine learning (ML) and its application to Economics and Finance. The main goal of this module is to enable you to understand how machine learning tools can complement the tools of traditional econometrics and how to apply these techniques to real-world economics and finance problems. By the end of this module you will have: 1. Developed a comprehensive understanding of key concepts in modern machine learning (ML): classification, prediction, supervised and unsupervised learning 2. Demonstrated a critical understanding of the advantages and disadvantages of ML as compared to traditional econometric approaches 3. Applied ML to real-world economics and finance problems, with examples based on: i. Prediction ii. Causal Inference and Policy Evaluation

What are the issues regarding different types of panel datasets? Or problems with survey methodology? Understand longitudinal data analysis by using micro-econometric techniques and critically examine survey methodology issues, like response rate and sampling frames. Apply panel data methods to study labour markets, focusing on marriage, unemployment and wages.