This unit provides an introduction to basic concepts in corporate finance and capital markets. It is designed to equip students to undertake further studies in finance. After reviewing some very basic ideas in finance and financial mathematics, the unit provides an introduction to the valuation of equity and debt securities and companies. The unit then examines issues related to pricing in capital markets and ends with a discussion of theory and practice related to capital structure and dividend policy.

This unit highlights the importance of statistical methods and tools for today's managers and analysts, and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

This unit provides students with an introduction to quantitative models and techniques in finance. Topics covered include basic stochastic calculus, probability measures and the role of numeraires, Black-Scholes and Hull-White models, and the theoretical and numerical techniques for valuing derivatives. There is a focus on both the intuitive and mathematical understanding of these topics, as well as their application to problems in quantitative finance.

Time series and statistical modelling is a fundamental component of the theory and practice of modern financial asset pricing as well as financial risk measurement and management. Further, forecasting is a required component of financial and investment decision making. This unit provides an introduction to the time series models used for the analysis of data arising in financial markets. It then considers methods for forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of financial return data; the Capital Asset Pricing Model (CAPM); financial return factor models, with known and unknown factors, in panel data settings; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting market risk measures such as Value at Risk. Emphasis is placed on applications involving the analysis of many real market datasets. Students are encouraged to undertake hands-on analysis using an appropriate computing package.

This unit covers the fundamentals of asset pricing and valuation, under equilibrium conditions and under no-arbitrage restrictions. It reviews the main themes in modern asset pricing, and introduce ideas of importance to the evolution of the discipline, and consequently of relevance to a practitioner's long term perspective. The unit emphasises quantitative methods, so students are required to have fairly strong mathematical skills. Nevertheless, the mathematical tools needed in the unit are adequately reviewed.

This unit covers several aspects of modern/post modern portfolio theory.An introduction to mathematical optimisation techniques in the presence of uncertainty is covered and results from modern portfolio theory to the Capital Asset Pricing Model derived. The unit also examines other popular models such as the Arbitrage Pricing Theory and Black-Litterman Model and concludes with some topical examples from industry. There is a degree of mathematical sophistication associated with this unit and consequently students should be comfortable with a mathematical approach. However, the required mathematical tools are covered in the unit.

This unit covers the concepts required for investment in fixed income securities, managing bond portfolios and understanding debt markets. Topics covered include duration, convexity, interest rate risk, bond volatility and the term structure of interest rates. The more complex types of debt securities studied include mortgage backed securities, corporate bonds with embedded options such as convertible bonds and interest rate derivatives.