This is a first course in empirical work on the asset pricing side of financial economics. This involves a combination of financial and econometric theory, and getting dirty working with data. The field itself is vast, but we will focus on two core ideas: 1) time series properties of asset returns (predictability, volatility, correlations with other variables etc) and 2) cross-sectional properties of asset returns implied by equilibrium asset pricing models (including CAPM, consumption-based asset pricing, factor models etc).
We'll also briefly look at some simple term structure models. The course does not do any empirical tests of derivative pricing models, and concentrates on discrete-time methods. We leave derivatives and continuous-time methods to other courses. To examine these ideas, we will use a variety of econometric techniques, including maximum likelihood, GMM, Bayesian methods, and various time-series models, including ARMA, GARCH, and regime-switching. The course is designed for first year doctoral students in finance. Economics doctoral students and other finance doctoral students are also welcome. Other students may take this course if they have previously taken at least one PhD-level finance course on asset pricing and one PhD-level course on statistics or econometrics.
David L. and Elsie M. Dodd Professor of Finance
Professor Santos' research focuses on two distinct areas. A first interest is the field of asset pricing with a particular emphasis on theoretical and empirical models that can account for the predictability of returns, both in the time series and the cross section. A second interest of Professor Santos is applied economic theory, specifically, the economics of financial innovations as well as theory of...