London School of Economics

The course provides an introduction into monetary and financial history from the 18th century to the present day. It examines the main developments in international monetary architecture and the global financial system since the Glorious Revolution. The course introduces students to major concepts of money and finance (financial development, financial integration, monetary policy, banking crises etc.) and to provide a long run perspective to the current policy debate.

The course covers key statistical methods and data analytic techniques most relevant to finance. Hands-on experience in analyzing financial data in the “R” environment is an essential part of the course. The course includes a selection of the following topics: obtaining financial data, low- and high-frequency financial time series, ARCH-type models for low-frequency volatilities and their simple alternatives, Markowitz portfolio theory and the Capital Asset Pricing Model, concepts and  practices in machine learning as applied in financial forecasting, Value at Risk. The course covers classification techniques using random forests and simple trading strategies if time permits.

Throughout the 18th century, thousands of young British men and women embarked on extensive journeys to continental Europe – an activity known as the Grand Tour.  This course explores who these people were, where they went, and the reasons for their expeditions.  For some, the Tour was the final stage of formal education; to others an opportunity for sexual adventures and pleasure-seeking. The course discusses the practical challenges of 18th-century travel, the political, religious, and cultural contexts of the Tour, as well as the key places to visit and the reasons for their popularity.  It also considers what the Tourists brought back with them: from physical artefacts for public and private collections, to new ways of seeing and understanding the world. The course introduces students to the actual writings of the Tourists, showing how they experienced international travel and shaped the modern tourist industry.

Data science and machine learning are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered. 

The course introduces students to basic principles of artificial intelligence (AI) systems. By AI, we refer to machines (or computers) that mimic cognitive functions that humans associate with the human mind, such as learning and problem solving. The course takes a practical approach, explaining the main principles and methods used in the design of AI systems. The course provides an introduction to main principles of deep learning, covering topics of neural nets as universal approximators, design of neural network architectures, backpropagation and optimization methods for training neural networks, and some special deep neural network architectures commonly used for solving AI tasks such as image classification, sequence modelling, natural language processing and generative models. If time allows, this course also provides an introduction to reinforcement learning problem formulation. Students gain practical knowledge to learn and evaluate deep learning and reinforcement learning algorithms (if time allows) using Python and open-source software libraries.

The first part of the course focuses on the foundations of public economics. The course introduces classical theories concerning equity, efficiency, and the rationales for government intervention in the economy. Students discuss market failure in the context of public goods and externalities, including environmental policy. They also discuss problems of public choice and political economics, and the implications of recent research in behavioral economics for policymaking. Students also study modern empirical methods that are used to evaluate the causal effects of public policies. The second part of the fall term considers social insurance policies, including unemployment insurance, disability insurance, retirement pensions, and public health insurance. Students study the economic rationale for government intervention in social insurance and the optimal type and extent of interventions, and they relate this to empirical evidence on the causal effects of changes in social insurance policies. In the spring term, the course is devoted to tax and transfer programs. The course begins by examining the incentive effects of taxes and transfers on labor supply, and then goes on to consider migration, tax avoidance, and tax evasion. As inequality is a key input to tax and transfer policy, we critically examine the measurement of and trends over time in income and wealth inequality in various countries. We look at the effect of taxation on economic efficiency and explore the optimal taxation of commodities and income. Finally, we consider questions of tax administration and apply them in a developing country context.

The course broadens, and selectively deepens, students' understanding of finance, building on their existing knowledge of financial economics. The course covers a broad range of topics, with both a theoretical and an empirical emphasis. These include topics in corporate finance, investments and performance evaluation, and international finance. The course consists of two interchangeable ten-week components, one on investments and international finance, and the other on corporate finance.

This course focuses on derivatives, with a particular emphasis on equity derivatives (standard call and put options, exotic options), futures and forward contracts, and interest rate derivatives (swaps, caps and floors, swaptions). It systematically addresses three basic questions: how do these products work, i.e. what are their payoffs? How can they be used, for hedging purposes or as part of trading strategies? And above all: how are they priced? The course emphasises a small number of powerful ideas: absence of arbitrage, replication, and risk-neutral pricing. These are typically introduced in the context of discrete-time models, but the course also covers some well-known continuous-time models, starting with a comprehensive treatment of the Black-Scholes model. The level of mathematics is appropriate for third-year students with a solid quantitative background.

The course examines the successes and failures of British business and industry, with an emphasis on the post-World War II period. It assesses many of the hypotheses on why the UK economy grew more slowly than other OECD nations during this period. Explanations of relative economic decline are examined in the context of comparisons with other European nations and with the US, Japan and more recently China. The course is organized to combine major economic and political themes, such as de-industrialization, globalization, education and training, management organization and practices, labor relations, and Britain’s relationship with the EEC/EU, with case studies of industries as diverse as textiles, motors, banking, pharmaceuticals, and steel. By interacting themes and case studies, students get a sense of how national policies can affect business opportunities, and how governments can both aid and harm business. The impact of government policies such as nationalization/privatization, regional policy and competition are also examined in this context. The primary focus is on the post-World War II period, including current changes in performance, but the historical roots of Britain's recent performance are also considered.

This course is an introduction to economic principles in the analysis of environmental change and natural resource use and in designing appropriate policy responses. The first part of the course primarily covers the concepts and tools of environmental and resource economics, such as the evaluation of regulatory and market-based instruments in controlling pollution; moral suasion and voluntary regulation; the economics of renewable resources (e.g. fisheries); the economics of non-renewable resources (e.g., fossil fuels and minerals). The second part applies these concepts and tools to provide an economic perspective on real-world policy issues. Topics covered include the following: cost-benefit analysis and environmental valuation; stated and revealed preferences methods (and some behavioral considerations); sustainable development; biodiversity; climate change; energy; directed technological change and green innovation; health and the environment.