This class exists in these courses :
• IÉSEG > MSc in Finance > Semester 1 > 2,00 ECTS

Prerequisites

Students are expected to have a general understanding of the following concepts: random variables; basic probability distributions such as Gaussian, Student t, Chi-square and Fisher distributions; statistics such as expectation/mean, variance/standard deviation, correlation; hypothesis testing, p-values (i.e. critical thresholds for tests) and confidence intervals.

Learning outcomes

Breakdown complex organizational problems using the appropriate methodology
Construct expert knowledge from cutting-edge information
Demonstrate an expertise on key concepts, techniques and trends in their professional fieldBe a reference point for expertise-related questions and ambiguities

Course description

Based on real datasets and practical applications (on Excel and Eviews), the lectures will cover the following topics:
- Correlation tests to check for linearity between variables;
- Simple linear regression;
- Multiple linear regression;
- Graphical investigation of breaks in time series and usage of dummy variables;
- Validation of regressions (i.e. fulfillment of key OLS assumptions, residuals’ autocorrelation and heteroskedasticity)
- Use and limitations of linear regressions

Class type

Class structure

Teaching methods

• Case study
• Coaching
• E-learning
• Interactive class
• Project work
• Tutorial

Assessment

1) Financial econometrics project (each group of students works on a selected dataset).
2) A final exam composed of problems, course questions (students can bring a two-sided A4 cheat sheet only devoted to valuation formulas), and in class applications using Eviews to analyse a given dataset.

Recommended reading

• R.A. DeFusco, D.W. McLeavey, J.E. Pinto, M.J.P. Anson, D.E. Runkle, 2015, Quantitative Investment Analysis, 3rd Edition, Wiley -