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.

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.

Although financial and monetary systems in China may appear similar to those in the U.S., China's hybrid economic model ensures their distinct operation. As China enters a new growth phase focusing on high-quality development, the importance of these systems in capital allocation and supporting government policies has grown. This course aims to explore these systems' structures and mechanisms, offering insights into investment opportunities and understanding the risks and challenges facing the world's second-largest economy.

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.

This course covers the basic concepts of measuring, modeling and managing risks within a financial optimization framework. Topics include single and multi-stage financial planning systems. Implementation from several domains within asset management and goal based investing. Machine learning algorithms are introduced and linked to the stochastic planning models. Python and optimization exercises required.