MBA Courses
FINANCE 632: Empirical Dynamic Asset Pricing
This course explores the interplay between dynamic asset pricing theory, statistical assumptions about sources of risk, and the choice of econometric methods for analysis of asset return data. Therefore, the lectures will be a blend of theory, econometric method, and critical review of empirical studies. Both arbitrage-free and equilibrium preference-based pricing models will be discussed, with particular emphasis given to recent developments and outstanding puzzles in the literature. The prerequisites for F632 are MGTECON 603 – 604, Finance 620, Finance 622, and Finance 625. In particular, I will assume familiarity with dynamic asset pricing theory, at the level of F622; and large-sample theory for least-squares, generalized method-of-moments, and maximum likelihood estimation methods. We will review these methods in the context of specific applications, but this material will not be developed in depth.
FINANCE 625: Empirical Asset Pricing
This course is an introduction to empirical research in asset pricing. The focus of the course is on the interplay between financial economic theory, econometric method, and that analysis of financial market data. Topics include tests of asset pricing models, return predictability in time-series and cross-section, empirical studies of asset market imperfections, and studies of individual and professional investor behavior. Class discussions will draw on textbooks/monographs and original articles and working papers.
Executive Education
- Stanford Visa SAFEA program, 2012–2016.
- Empirical Asset Pricing, International Monetary Fund, 2002, 2003, 2004.
- Credit Risk Management and Pricing (multiple offerings).
- Market and Credit Risk Management for Financial Institutions (Summer 1997, Spring, 1998, Spring, 1999).
- Stanford Financial Management Program
- London-Stanford International Investment Management Program, July 1988-1991.
- Nomura Management School (Japan) Finance Program, October 1992-1995.
- US Trust Program, Stanford, 2007, 2008.