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

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ILOs

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Normally, the group-based assessment listed above cannot be reassessed.

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.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.