Postgraduate taught 

Financial Modelling & Investment MSc

Empirical Asset Pricing ECON5069

  • Academic Session: 2023-24
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No

Short Description

 This course explores the interplay among financial economic theory, the availability of relevant data, and the choice of econometric methodology in the empirical study of asset pricing models. This course includes asset pricing models such as the capital asset pricing model (CAPM), arbitrage pricing theory (APT), stochastic discount factor (SDF), linear multifactor pricing models, unconditional and conditional consumption based models CAPM (CCAPM) and term structure models. Econometric models such as time series regression, multivariate regression, seemingly unrelated regression (SURE) and generalised method of moments (GMM) are introduced for the empirical test of the asset pricing models. The practical demonstration of empirical asset pricing tests using the R programming language is an important component of the course.


10 weekly lectures in 2h blocks

5 computer labs at 1h each

Excluded Courses



Basic Econometrics (ECON5002), Portfolio Analysis and Investment (ECON5027) or Economic Fundamentals and Financial Markets (ECON5005).


ILO (covered)

Course Aims

The main aim of this course is to introduce students to the core fields of modern finance and financial econometrics. This course overviews asset pricing models and introduces econometrics for the empirical test of asset pricing models. Furthermore, this course emphasises how students construct datasets and test asset pricing models using the R programming language.

Intended Learning Outcomes of Course

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

1. Critically assess and work with discrete time empirically testable asset pricing models

2. Perform modern econometric methods

3. Formulate econometric models for data analysis.

4. Work with datasets and perform empirical testing

5. Use the R programming language to solve statistical and numerical problems.

6. Work collaboratively in a group to produce a combined piece of coursework, by liaising with other class members, allocating tasks and co-ordinating group meetings.

Many of the methods presented in this course apply to other areas in economics and finance.


Minimum Requirement for Award of Credits

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