Empirical Asset Pricing I

FIN511 Empirical Asset Pricing I

  • Topics

    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

    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

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

  • Required prerequisites

    Required prerequisites

    PhD level course in theoretical asset pricing.

  • Requirements for course approval

    Requirements for course approval

    Class Participation

  • Assessment

    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

    Grading Scale

     Grades A - F

  • Computer tools

    Computer tools

    Matlab, R, LATEX

  • Semester

    Semester

     Spring.

  • Literature

    Literature

    Lecture notes.

    John Cochrane: Asset Pricing, Princeton University Press,

Overview

ECTS Credits
4
Teaching language
 English.
Semester
Spring

Course responsible

Professor Bernt Arne Ødegaard, Department of Finance