MSc — 2027 entry Economics and Finance

This module introduces students to microeconometric models for limited dependent variables, panel data methods, and causal inference techniques. A strong emphasis is placed on the empirical applications of these techniques and their intuition.

This module is targeted at students interested in understanding the pricing of derivative securities, specifically options. Applications are used to reinforce a rigorous development of arbitrage theory that underpins the pricing of derivative securities.

This module further develops finance for students interested in corporate finance. The module is to demonstrate the link between those various decisions and areas. In particular, we will explore how the firm’s financing, investment, capital structure and payout policies interact with each other and how those decisions have implications for corporate bankruptcy and takeovers.

Risk management focuses on the following types of financial risk that arise for a business: market risk, credit risk and liquidity risk. A brief introduction to non-financial risks is also given, but is not the main focus. This module considers financial risk management strategies; how risk is evaluated; the methods that are available to hedge against risk and the strategies that are employed to mitigate risk. This is complemented by consideration of key risk management issues for financial institutions including bank capital adequacy. The lessons to be learned from the Financial Crisis of 2007-08 are also investigated. Understanding risk and how to identify and deal with it provides a key input for students of finance, accounting or banking.

This module introduces the core building blocks of modern finance theory and examines how financial markets operate in practice. It explores the functioning of capital markets, options markets, and foreign exchange markets, and covers key topics including investor behaviour, asset pricing, and financial trading. The module emphasises the application of financial models to interpret market outcomes and understand the mechanisms that drive prices and trading in global financial markets.

This module looks at the firm and the industrial environment in which it operates. It asks why the firm exists and how it chooses to organise itself, before considering some of the factors that affect its performance and how these are related to the market structure in which it operates. An important influence on the firm is the structure of the market in which the firm works. This will affect the products and the process that the firm selects and the profits it earns. At the same time, of course, the firm may take actions that affect this market structure to its advantage. Thus, we want to ask how the firm¿s performance is affected by the market it is in. This has important competition and industrial policy implications.

The course is mainly aimed at reviewing modern theories of international finance, with special reference to developing economies. This module starts with micro foundation of macroeconomics, that is, intertemporal optimization. Then it exposes students fundamental topics of international finances such as risk sharing, exchange rate, and currency crises. It also emphasizes the importance of empirical analysis with recent econometric methods.

The module equips students with the recent machine and statistical learning toolkit for modern empirical economics and quantitative finance analysis. It is practical in orientation and rigorous in execution, developing the professional competence to implement state-of-the-art methods, interpret their output critically, and communicate findings to both technical and non-technical audiences - skills of direct relevance to careers in asset management, central banking, economic consultancy, and policy institutions.The core techniques are introduced in sequence, each grounded in statistical theory and connected to professional practice.  For instance, students will study dimension reduction, encompassing regularisation methods such as ridge regression, the LASSO, the elastic net, and factor models. They will learn how these tools are used in asset management to select pricing factors and in central banks and forecasting institutions to handle large macroeconomic datasets. Students will study tree-based ensemble methods, including random forests and gradient boosting, and learn how they are applied in credit risk departments,  quantitative hedge funds, and policy research to capture non-linear relationships that standard econometric models miss. Students will also study kernel methods, including support vector machines and generalised additive models, and learn how these are used wherever an economic relationship requires a flexible, interpretable specification that does not impose an arbitrary functional form. Finally, students will study rigorous model evaluation - cross-validation, multiple-testing corrections, and model confidence sets - and understand why these procedures are a professional requirement in any setting where analytical findings inform decisions.Throughout, statistical foundations are developed carefully alongside applications, so that students understand not only how to use each method but when it will fail and at what cost.

This module introduces students to microeconometric models for limited dependent variables, panel data methods, and causal inference techniques. A strong emphasis is placed on the empirical applications of these techniques and their intuition.

This module is targeted at students interested in understanding the pricing of derivative securities, specifically options. Applications are used to reinforce a rigorous development of arbitrage theory that underpins the pricing of derivative securities.

This module further develops finance for students interested in corporate finance. The module is to demonstrate the link between those various decisions and areas. In particular, we will explore how the firm’s financing, investment, capital structure and payout policies interact with each other and how those decisions have implications for corporate bankruptcy and takeovers.

Risk management focuses on the following types of financial risk that arise for a business: market risk, credit risk and liquidity risk. A brief introduction to non-financial risks is also given, but is not the main focus. This module considers financial risk management strategies; how risk is evaluated; the methods that are available to hedge against risk and the strategies that are employed to mitigate risk. This is complemented by consideration of key risk management issues for financial institutions including bank capital adequacy. The lessons to be learned from the Financial Crisis of 2007-08 are also investigated. Understanding risk and how to identify and deal with it provides a key input for students of finance, accounting or banking.

This module introduces the core building blocks of modern finance theory and examines how financial markets operate in practice. It explores the functioning of capital markets, options markets, and foreign exchange markets, and covers key topics including investor behaviour, asset pricing, and financial trading. The module emphasises the application of financial models to interpret market outcomes and understand the mechanisms that drive prices and trading in global financial markets.

This module looks at the firm and the industrial environment in which it operates. It asks why the firm exists and how it chooses to organise itself, before considering some of the factors that affect its performance and how these are related to the market structure in which it operates. An important influence on the firm is the structure of the market in which the firm works. This will affect the products and the process that the firm selects and the profits it earns. At the same time, of course, the firm may take actions that affect this market structure to its advantage. Thus, we want to ask how the firm¿s performance is affected by the market it is in. This has important competition and industrial policy implications.

The course is mainly aimed at reviewing modern theories of international finance, with special reference to developing economies. This module starts with micro foundation of macroeconomics, that is, intertemporal optimization. Then it exposes students fundamental topics of international finances such as risk sharing, exchange rate, and currency crises. It also emphasizes the importance of empirical analysis with recent econometric methods.

The module equips students with the recent machine and statistical learning toolkit for modern empirical economics and quantitative finance analysis. It is practical in orientation and rigorous in execution, developing the professional competence to implement state-of-the-art methods, interpret their output critically, and communicate findings to both technical and non-technical audiences - skills of direct relevance to careers in asset management, central banking, economic consultancy, and policy institutions.The core techniques are introduced in sequence, each grounded in statistical theory and connected to professional practice.  For instance, students will study dimension reduction, encompassing regularisation methods such as ridge regression, the LASSO, the elastic net, and factor models. They will learn how these tools are used in asset management to select pricing factors and in central banks and forecasting institutions to handle large macroeconomic datasets. Students will study tree-based ensemble methods, including random forests and gradient boosting, and learn how they are applied in credit risk departments,  quantitative hedge funds, and policy research to capture non-linear relationships that standard econometric models miss. Students will also study kernel methods, including support vector machines and generalised additive models, and learn how these are used wherever an economic relationship requires a flexible, interpretable specification that does not impose an arbitrary functional form. Finally, students will study rigorous model evaluation - cross-validation, multiple-testing corrections, and model confidence sets - and understand why these procedures are a professional requirement in any setting where analytical findings inform decisions.Throughout, statistical foundations are developed carefully alongside applications, so that students understand not only how to use each method but when it will fail and at what cost.

This module introduces students to microeconometric models for limited dependent variables, panel data methods, and causal inference techniques. A strong emphasis is placed on the empirical applications of these techniques and their intuition.

This module is targeted at students interested in understanding the pricing of derivative securities, specifically options. Applications are used to reinforce a rigorous development of arbitrage theory that underpins the pricing of derivative securities.

This module further develops finance for students interested in corporate finance. The module is to demonstrate the link between those various decisions and areas. In particular, we will explore how the firm’s financing, investment, capital structure and payout policies interact with each other and how those decisions have implications for corporate bankruptcy and takeovers.

Risk management focuses on the following types of financial risk that arise for a business: market risk, credit risk and liquidity risk. A brief introduction to non-financial risks is also given, but is not the main focus. This module considers financial risk management strategies; how risk is evaluated; the methods that are available to hedge against risk and the strategies that are employed to mitigate risk. This is complemented by consideration of key risk management issues for financial institutions including bank capital adequacy. The lessons to be learned from the Financial Crisis of 2007-08 are also investigated. Understanding risk and how to identify and deal with it provides a key input for students of finance, accounting or banking.

This module introduces the core building blocks of modern finance theory and examines how financial markets operate in practice. It explores the functioning of capital markets, options markets, and foreign exchange markets, and covers key topics including investor behaviour, asset pricing, and financial trading. The module emphasises the application of financial models to interpret market outcomes and understand the mechanisms that drive prices and trading in global financial markets.

This module looks at the firm and the industrial environment in which it operates. It asks why the firm exists and how it chooses to organise itself, before considering some of the factors that affect its performance and how these are related to the market structure in which it operates. An important influence on the firm is the structure of the market in which the firm works. This will affect the products and the process that the firm selects and the profits it earns. At the same time, of course, the firm may take actions that affect this market structure to its advantage. Thus, we want to ask how the firm¿s performance is affected by the market it is in. This has important competition and industrial policy implications.

The course is mainly aimed at reviewing modern theories of international finance, with special reference to developing economies. This module starts with micro foundation of macroeconomics, that is, intertemporal optimization. Then it exposes students fundamental topics of international finances such as risk sharing, exchange rate, and currency crises. It also emphasizes the importance of empirical analysis with recent econometric methods.

The module equips students with the recent machine and statistical learning toolkit for modern empirical economics and quantitative finance analysis. It is practical in orientation and rigorous in execution, developing the professional competence to implement state-of-the-art methods, interpret their output critically, and communicate findings to both technical and non-technical audiences - skills of direct relevance to careers in asset management, central banking, economic consultancy, and policy institutions.The core techniques are introduced in sequence, each grounded in statistical theory and connected to professional practice.  For instance, students will study dimension reduction, encompassing regularisation methods such as ridge regression, the LASSO, the elastic net, and factor models. They will learn how these tools are used in asset management to select pricing factors and in central banks and forecasting institutions to handle large macroeconomic datasets. Students will study tree-based ensemble methods, including random forests and gradient boosting, and learn how they are applied in credit risk departments,  quantitative hedge funds, and policy research to capture non-linear relationships that standard econometric models miss. Students will also study kernel methods, including support vector machines and generalised additive models, and learn how these are used wherever an economic relationship requires a flexible, interpretable specification that does not impose an arbitrary functional form. Finally, students will study rigorous model evaluation - cross-validation, multiple-testing corrections, and model confidence sets - and understand why these procedures are a professional requirement in any setting where analytical findings inform decisions.Throughout, statistical foundations are developed carefully alongside applications, so that students understand not only how to use each method but when it will fail and at what cost.