Introduction to asset pricing covering theory in both continuous and discrete time to study dynamic portfolio choice; derivative pricing; the term structure of interest rates; and intertemporal asset-pricing and consumption-based models.

An introduction to the modern theory of asset pricing. Topics include: No arbitrage, Arrow-Debreu prices and equivalent martingale measure; security structure and market completeness; mean-variance analysis, Beta-Pricing, CAPM; and introduction to derivative pricing.

This course covers a number of advanced topics related to asset management and asset pricing. Topics covered include mean-variance analysis, dynamic optimization, CAPM, APT, market efficiency, active money management, indexing, stock return predictability, bubbles and crashes, mutual fund and professional managers' performance, hedge funds, security analysts, social interaction and investor behavior and fixed income portfolio management.

This course studies the impact of recent technological innovations in the financial sector. We first study developments in digital money, ledgers, and payment systems. We then study the use of big data and artificial intelligence in the provision of credit. Finally, we explore different possible futures for how the overall financial sector could be organized. Throughout the course, students learn both the underlying economics of fintech as well as practical statistical tools for developing new fintech products. The recent financial innovations are also put into their historical, social and ethical context.

The course introduces financial derivatives and their pricing. The pricing techniques encompass the Black-Scholes formula as well as extensions to accommodate time-varying volatility and more complex contracts. We also devote great attention to discussing the roles played by derivatives in shaping financial markets and the real economy using the currency markets as a focal point. This course is technical by nature, and requires extensive use of calculus, statistics, machine learning, and python programming. Weekly homework includes paper-and-pencil problems, numerical and data-driven work using python, and case studies.

In this course, students carry out research advised by an affiliated faculty member of the BCF. The objective is to study a problem in finance or economics, with initial data collection and its subsequent analysis including the implementation of methods potentially useful to solve the problem. Students should write a final report that will be graded by the advisor.

Financial institutions play an increasingly dominant role in modern finance. This course studies the financial system and its protagonists, with a focus on efficiency and stability. It covers important theoretical concepts and recent developments in asset pricing under asymmetric information, market microstructure, and financial intermediation. Topics include market efficiency, liquidity crises, asset price bubbles, herding, risk management, market design and financial regulation.

This course covers theoretical and practical aspect of alternative investments, examples of which include hedge fund, venture capital, private equity, and real estate investments. These forms of investments play an increasingly important role in US capital markets, and this course provides students with the tools necessary to understand the role of these alternative investments and how to integrate them when constructing optimal wealth portfolios.

This course offers a comprehensive and modern view of a risk management system. The material studied will be helpful for any future career related to trading, portfolio and risk management. It is a hands-on computational course that mixes theory with practical solutions to issues appearing in financial firms. We primarily cover topics related to market risk including but not limited to risk factors and how they are used to price assets, bonds and options hedging, portfolio conditional loss distributions, back-testing our risk management models, and stress testing.

The course is divided into three parts of approximately the same lengths. Density estimation (heavy tail distributions) and dependence (correlation and copulas). Regression analysis (linear and robust alternatives, nonlinear, nonparametric,classification.) Machine learning (TensorFlow, neural networks, convolution networks and deep learning). The statistical analyzes, computations and numerical simulations are done in R or Python.

The intent of this course is to introduce the student to the technical and algorithmic aspects of a wide spectrum of computer applications currently used in the financial industry, and to prepare the student for the development of new applications. The student is introduced to C++, the weekly homework involves writing C++ code, and the final project also involves programming in the same environment.