This course provides an in-depth exploration of both the theoretical foundations and empirical methods used in asset pricing. Students will examine the interplay between financial economic theory, the availability of relevant data, and the choice of econometric methodology in testing and applying asset pricing models.

Key theoretical models covered include the capital asset pricing model (CAPM), arbitrage pricing theory (APT), linear multifactor pricing models, unconditional and conditional consumption-based CAPM (CCAPM), and term structure models. On the empirical side, students will engage with econometric techniques such as time series regression, multivariate regression, seemingly unrelated regression (SURE), and the generalized method of moments (GMM) to test these models using real-world data.

The course emphasizes practical application, with hands-on empirical testing of asset pricing models using R/Python. In addition to traditional written reports, students will also communicate their findings in the form of podcasts, developing skills for effectively presenting complex financial concepts in modern, professional formats.

Synchronous:
10 x 2-hour lectures on campus
5 x 1-hour labs online

None

None

1. Oral assessment & presentation; Group; 8 minutes (podcast); 25%; ILOs 1, 5.

2. Degree exam: in-person; Individual; 120 minutes; 75%; ILOs 1-3.

It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.

[No exceptions]

The course aims to:

1. Provide students with a strong foundation in asset pricing theory and the econometric methods used for empirical testing.

2. Equip students with practical skills to construct datasets and empirically test asset pricing models using the R programming language.

3. Enable students to apply theoretical models to real-world financial data, fostering critical analysis and professional communication of empirical results.

By the end of this course students will be able to:

1. Critically evaluate and contrast discrete time asset pricing models.

2. Formulate econometric models for data analysis.

3. Process large financial datasets, then implement and interpret advanced empirical tests.

4. Use R/Python programming language to solve statistical problems.

5. Plan, coordinate and produce a combined piece of work that conveys empirical asset pricing insights demonstrating effective team collaboration and integration of individual contributions.