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13178 - Financial Econometrics

Academic Year 2024/2025

                
                        Docente:
                        Mirco Balduini
                    
                        Credits:
                        6
                    
                        SSD:
                        SECS-P/05
                    
                        Language:
                        Italian
                    
                        Teaching Mode:
                        Traditional lectures
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Financial Markets and Institutions (cod. 0901)

                            Online Lessons
                        
                            Teaching resources on Virtuale
                        
                                    Course Timetable
                                
from Feb 13, 2025 to May 23, 2025

Learning outcomes

At the end of the course, the student will be familiar with the main econometric tools used in the analysis of both linear and nonlinear models, as well as models for quantitative and qualitative variables frequently applied in empirical finance.

In particular, students will use inference techniques with Maximum Likelihood (in addition to OLS and GLS) and apply them to limited dependent variables, ARCH models, and stochastic discount factor models.

All applications will be conducted using one of the most widely used econometric software packages (Stata, R, or Python), depending on the class’s preference.

Course contents

Linear Regression and Least Squares. LogisticRegression. Time Series Analysis: stationarity, ARMA models, and tests. Estimation of the Capital Asset Pricing Model (CAPM) and other financial applications.
Volatility Models: ARCH, GARCH, and asymmetric volatility.
Multivariate Time Series Analysis: vector autoregressive (VAR) models.
Risk Assessment Tools: Value at Risk and Monte Carlo simulations.

Readings/Bibliography

The material presented during the course (slides, articles, software code) represents the main source for studying the topics covered.

For a review of the basic methods:

Stock, J.H. and Watson, M.W. (2020), Introduction to Econometrics, 4th edition, Pearson.

For the time series section:

Pesaran, H. (2015), Time Series and Panel Data Econometrics, 1st edition, Oxford University Press, chapters 6, 12–13, 14–16, 19–22.

For a more advanced reference:

Tsay, R. (2010), Analysis of Financial Time Series, 3rd edition, Wiley.

Teaching methods

The teaching methods include lectures and hands-on exercises with software (Python).

Assessment methods

Assessment is based on a written exam at the end of the course and a problem set assigned during the course.

The final grade is calculated as a weighted average using the following weights:

Problem set (to be completed at home): 30%
Final exam: 70%

The final exam is written and lasts 1 hour.

The maximum grade is 30 cum laude, awarded when all answers are correct and complete.

The grading scale is as follows:

Below 18: insufficient
18–23: sufficient
24–27: good
28–30: very good
30 cum laude: excellent

Teaching tools

Course webpage on the Virtuale platform (virtuale.unibo.it), where the following will be regularly provided:

Software materials related to the empirical applications covered in class.
References to academic and press articles of particular relevance to the course topics.

Office hours

See the website of Mirco Balduini