Past courses - 2020

There is growing recognition that central banks play an important role in financial markets and the financial system, not only in terms of setting short term interest rates, but also derived from the role of central bank money (liquidity). The overall objective of this course is to enhance students’ understanding of this emerging topic and to stimulate to research. To that end, we will study papers that shed light on the role of money in the financial system, the functioning of the market for liquidity, how it interacts with the broader financial markets, and the impact of central bank policies, including collateral policy. Class sessions will focus on selected empirical and theoretical papers and also introduce relevant institutional material. The course is suited for students that have completed basic PhD level core courses in finance or economics and have an interest in conducting research in the topics of the course or related areas. Assessment is in the form of a research proposal and class presentations.

More information can be found here.

The course will be postponed to a later date.

The course gives the student a fundamental understanding of both elementary and advanced statistical and machine learning methods used in finance, with a focus on applications and research methods. The course gives the students a thorough introduction to topics such as ridge regression, the Lasso, Adaptive Lasso, Bridge regression, Regression Trees, Random Forest, and Neural Networks. The course gives a thorough understanding of methods such as the ordinary least squares, its shortcomings, and how, for example, machine learning can improve the estimates and their errors. Machine learning typically prescribes a vast collection of high-dimensional models attempting to predict quantities of interest while imposing regularization methods. More information on course website.

The course will be postponed to a later date.

This workshop exposes students to recent developments in the literature on the theory and management of hedge funds. It is intended for doctoral students in finance and related fields. Topics include hedge fund trading strategies, compensation, performance evaluation, liquidity management, operational risk, and the role of arbitrageurs in capital markets.

The object of this course is to provide an introduction to continuous time finance, including arbitrage theory, stochastic optimal control theory, and dynamic equilibrium theory. The course also contains an introduction to stochastic differential equations and Itô calculus, which are the main mathematical tools used in this field of research. More information on the course website and here.