FIN511 Empirical Asset Pricing I (expired)
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.
After completion of the course, the student's will be able to:
- 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.
- 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.
- 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 is concentrated in four two-day sessions throughout the spring.
PhD level course in theoretical asset pricing.
Requirements for course approval
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.
Grades A - F
Matlab, R, LATEX
John Cochrane: Asset Pricing, Princeton University Press,
- ECTS Credits
- Teaching language
Professor Bernt Arne Ødegaard, Department of Finance