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.

Under the direction of a Bendheim Center Affiliated Faculty member, students carry out a master's project, write a report, and present the results in the form of a poster or an oral presentation in front of an examining committee.

Traditional economics and finance typically use the simple "rational actor" model, where people perfectly maximize, and efficient financial markets. We will present models that are psychologically more realistic than this standard model. About 30% of the course will be devoted to economics, 70% to finance. Applications to economics will include decision theory, happiness, fairness, and neuroeconomics. Applications to finance will include theory and evidence on investor psychology, predictability of the stock market and other markets, limits to arbitrage, bubbles and crashes, experimental finance, and behavioral corporate finance

Risk management systems are a fundamental tool in investment banks and hedge funds. The objective of this quantitative course is to give students a comprehensive view of such a system. The course covers topics related to market, credit and operational risks (time allowing). We learn about risk factors and how they are used to mark assets to market, build portfolio conditional loss distributions and back-test them to learn if our models are adequate. We also study in detail portfolio and derivatives credit risk. Theoretical classes are complemented with hands-on computational homework and a take home computational midterm exam.

With its rapid economic growth in the past three decades, China already has the world's second largest economy. Meanwhile its financial markets are also being quickly liberalized and integrated with the rest of the world. As the current trend continues, there are growing interests to learn and understand the workings of China's financial and monetary systems. This course aims to serve this objective with a particular emphasis on understanding the role provided by the financial system in facilitating China's economic development, in addition to the investment opportunities and risk presented by the system to the outside world.

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.