FIN511 Empirical Asset Pricing I (expired)

• Topics

  Overview of the course

  • Toolchest for empirical work: Matlab / R.
  • Basic econometrics

  Linear regressions with applications.

  Maximum likelihood, example application: Binary Choce regressions

  • Investigating the cross section of asset returns.

  The history of crossectional analysis. From Fama-MacBeth to Fama-French.

  Tools to investigate risk-return tradeo : Regressions. Fama-MacBeth analysis. GMM. Principal Components (APT).

  The Fama-French analysis and current state of the art.

  • Investigating the Market Risk Premium.

  Basic Estimation. Representative Agent Modelling. The Equity Premium Puzzle. Non-parametrics: Hansen-Jagannathan bounds. Stochastic discount factor representation and estimation.

  • Event Studies.
  • Time Series.

  Univariate time series modelling. VARs. ARCH.

  Predictability and forecasting.

  • High Frequency (Market Microstructure) Data

  Issues. Realized Volatility.

  • Liquidity (Market Microstructure)

  Measurement using low and high-frequency data.

  Modelling interaction of prices and volume.

  Liquidity and Asset Pricing.

  • Using data in clever ways. Example: Di in Di .
  • Data Snooping.

• Learning outcome

  After completion of the course, the student's will be able to:

  Knowledge

  • Assess the common empirical research methods in nancial economics.
  • Understand in particular methods for testing asset pricing models.
  • Evaluate applications in corporate nance, time series and choice thory.
  • Identify the research frontier in empirical asset pricing.
  • Illustrate an understanding of current issues in market microstructure.
  • Demonstrate an awareness of the dangers of data-snooping and similar issues in model formulation and evaluation.

  Skills

  • Operate computer tools and languages common in empirical nance (Matlab/R)
  • Implement the common empirical methods in nance
  • Replicate analysis in state-of-the art empirical nance papers.

  General competence

  • Demonstrate the ability to write up the results of empirical investigations as done in academic articles.
  • Explain the results and implications for theory of empirical nance research.
  • Employ computer skills in common academic tasks.

• Teaching

  Teaching is concentrated in four two-day sessions throughout the spring.

• Required prerequisites

  PhD level course in theoretical asset pricing.

• Requirements for course approval

  Class Participation

• Assessment

  Assessment is based on student handins of assigned problem sets. The number of handins is between 3 and 6. The number and handin dates will be announced at the start of the lecture series.

• Grading Scale

   Grades A - F

• Computer tools

  Matlab, R, LATEX

• Literature

  Lecture notes.

  John Cochrane: Asset Pricing, Princeton University Press,