BUS326 Applied Financial Econometrics

Credits (ECTS):5

Course responsible:Daumantas Bloznelis

Campus / Online:Taught campus Ås

Teaching language:Engelsk

Course frequency:Annually.

Nominal workload:125 hours.

Teaching and exam period:This course starts in Autumn parallel. This course has teaching/evaluation in the autumn parallel.

About this course

This course covers econometric analysis of financial and commodity markets. It links up financial theory with real-world data from major financial databases to facilitate forecasting, optimization and risk management. Combining econometric theory with extensive software applications, the hands-on approach provides a head start for a master’s thesis in finance - and a glimpse into the work of a quantitative analyst.

The econometric topics to be covered include (the list is indicative):

  • Time series properties, leads, lags, autocorrelation, stationarity, unit roots and structural breaks;
  • Autoregressive moving-average (ARMA) model;
  • Vector autoregression (VAR) and Granger causality;
  • Cointegration and vector error correction (VEC) model; and
  • Volatility models such as GARCH.

Learning outcome


Students are familiar with

1. core ideas, principles and models in financial econometrics;

2. stylized facts of financial data;

3. selected sources of financial data;

4. the role of empirical analysis in the use, assessment and development of financial theory;

5. the relevance of econometrics in solving forecasting, optimization and risk management problems in finance.


Students can

1. formulate an econometric model based on financial theory and statistical properties of the data;

2. retrieve financial data from selected databases;

3. implement selected financial econometric models using appropriate software;

4. assess the estimated models’ statistical adequacy;

5. interpret the estimated models in the context of financial theory;

6. carry out selected analyses within financial forecasting, optimization and risk management;

7. present empirical findings and integrate financial econometric analysis into a research paper or a master’s thesis.

General competence:


1. appreciate the use of econometric analysis in developing and assessing financial theory;

2. can analyze and discuss forecasting, optimization and risk management problems in finance both conceptually and at the level of implementation;

3. can use R or other suitable software for retrieving, manipulating, analyzing and modelling financial data;

4. appreciate the limitations of empirical econometric analysis.

  • The course will consist of digital lectures and on-campus exercise sessions. Students should bring their own laptops to the exercise sessions and familiarize themselves with the use of available data and software for empirical analysis of financial and commodity markets.
  • Office hours by appointment.
  • 1. Basic econometrics

    2. Basic programming and data management (e.g. BUS350 or INF120)

    3. Basic finance and investment (BUS220 or equivalent)

    4. Financial investments and risk management (BUS322 or equivalent)

  • Semester-long project assignment to be delivered at the end of the semester will count for 100% of the grade. No re-sit examination will be arranged in this course.

  • External examiner will control the quality of syllabus and the content and assessment principles of the semester-long project assignment.
  • Mandatory attendance of at least 70% of the exercise sessions. Three individual mandatory activities (approved/not approved) consisting of an oral presentation/discussion and two individual written submissions. Approved mandatory activity is valid until and including the next time the course is given.
  • The course is taught in English.
  • 6 hours of teaching per week, distributed as follows:

    2 hours of digital teaching;

    2 hours of exercise sessions (on campus);

    2 hours of digital exercise sessions (for students abroad).

  • The course is intended for students enrolled in master programmes at NMBU. It is also open for other students with sufficient prior knowledge.