FIN545A Asset Pricing II

• Topics

  This course is an advanced PhD level course in empirical asset pricing. The asset pricing field is vast, but we will focus primarily on two core ideas:

  1. time-series properties of asset returns (predictability, volatility, correlations with other variables, etc.)
  2. cross-sectional properties of asset returns implied by equilibrium asset pricing models (including CAPM, consumption-based asset pricing, factor models, etc.)

  We will discuss these issues in the context of equity, currency, commodity and bond markets, as well as derivative markets. In addition to applying standard econometric techniques used in empirical asset pricing, including GMM and maximum likelihood, as well as various time-series models, we will also cover recent research on machine learning techniques as applied to asset pricing. We view these econometric techniques as a way of answering economic questions, rather than being interested in the econometric methodology per se.

• Learning outcome

  After successfully completing the course, the candidates can:

   Knowledge:

  • Discuss the current research frontier in asset pricing.

  Skills:

  • Formulate problems, plan and carry out original research within asset pricing.

  General competence:

  • Communicate and discuss research with a peer audience.
  • Conduct independent research on the topics of this course.

• Teaching

  A mix of regular and zoom lectures.

• Restricted access

  • PhD candidates at NHH
  • PhD candidates at Norwegian institutions

• Required prerequisites

  The prerequisites are a PhD level course in theoretical asset pricing, as well as an introductory class in empirical asset pricing and econometrics.

• Compulsory Activity

  One (very large) exercise set, as well as being present for each class.

  An original empirical asset pricing research project in the form of a 4-10 A4 pages long satisfactory write-up detailing the research question, the data used in the study, and the empirical results. 

• Assessment

  The final course grade is based on an individual in-class 20-minute presentation of the student's research project (100%)

• Grading Scale

  Pass/fail

• Computer tools

  You will need access to Matlab, R, or Python.

• Literature

  I will distribute detailed lecture notes in class. These notes constitute the required reading as there is no textbook that covers this material fully. You are encouraged to download copies of any journal articles I make reference to in these notes. A textbook reference is given below for fundamental background reading.

  Cochrane, John, 2005, Asset Pricing: Revised Edition Princeton, NJ: Princeton University Press