BUS326 Applied Financial Econometrics

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.

Knowledge

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.

Skills

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:

Students

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.