After completing this course students will have the ability to:

• understand and explain the principles and different theoretical models to price risky assets;
• understand and describe the differences between equilibrium and arbitrage theories and its consequences for asset pricing;
• describe the structure of a consumption based asset pricing model, the stochastic discount factor, and the classical equilibrium based capital asset pricing model;
• understand the differences between discount factors, betas and mean-variance-frontiers;
• understand the assumptions and the predictions of factor pricing models such as CAPM, and APT;
• apply regression based tests of the linear factor models;
• apply the Fama and French three factor model to adjust for risks of asset returns.

Apart from that, completing this course will contribute to the students’ ability to:

• work on a problem in pricing risky assets and accumulate experience that knowing the theory of asset pricing well is important to better understand the risk and return trade-off of financial assets;
• efficiently work and communicate in a team by having learned how to coordinate a group and team efforts;
• work to find solutions for challenging and complex practical problems in a team or a group and experience the advantages of economies of scale when a solution to a problem is found.

Moreover, after completing this course the student will have the ability to:

• identify interesting practical financial economics problems and relate their solutions to existing theoretical insights;
• work with empirical data to better understand some of the issues of asset pricing.

Compulsory attendance will be monitored through presentations of varying student teams, in-class tests, and active class participation. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

The course will combine two different ways to deliver the different topics to the students. The first one will be a classical lecture style approach where the instructor discusses the theory and its application of classical topics in asset pricing. The second one is a hands-on approach in which students have to apply the statistical software R to estimate single and multifactor asset pricing models.

The final course grade is a weighted sum of

• 80% final exam (at least 45% of the final must be positive)
• 20% classroom participation "extra points"
• 20% empirical project (at least 45% of the project must be positive)

All students have to take the final exam. At the end of the course, students have to work on an empirical project that has to be submitted according to the time schedule communicated in class.

For the extra points, students have to solve small problem sets which are discussed and presented in class. 1 hour before the lecture starts the corresponding exercises have to be marked by the clicker facility available at learn WU. In the case of insufficient performance in class, you don't get the extra points available for the current session. In the case, you marked exercises and do not show up in the course you lose the opportunity to get extra points by solving assignments.

Course prerequisites include:

• Sound knowledge of principles of finance, microeconomics and basic financial econometrics.
• Knowledge in analysis (in particular calculus, including Taylor’s rule and comparative static analysis).
• Knowledge in statistics (in particular hypothesis testing).
• Knowledge in optimization (in particular static optimization and basic knowledge in dynamic optimization).
• Interest in the pricing of risky assets.

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soegner@ihs.ac.at

Students will have access to a set of slides that cover the material discussed in class. Slides are available as pdf-files and can be downloaded from the course website.

Additional reading:

• Stephen J.Taylor, Asset Price Dynamics, Volatility and Prediction. Princeton University Press, 2005.
• Darrell Duffie, Dynamic Asset Pricing Theory. Second edition, Princeton University Press, 1996.