Teaching code

4S00241

Teacher

Diego Lubian

Coordinatore

Diego Lubian

Credits

9

Language

Italian

Scientific Disciplinary Sector (SSD)

SECS-P/05 - ECONOMETRICS

Period

primo semestre lauree magistrali dal Oct 1, 2018 al Dec 21, 2018.

Lessons timetable Moodle Seminars

Learning outcomes

Financial econometrics is the intersection of statistical techniques and finance. Financial econometrics provides a set of tools that are useful for modeling financial data and testing beliefs about how markets work and prices are formed.

Program

1. The simple linear regression model
2. The multiple linear regression model
3. Empirical evidence on security returns (single and multifactor models)
4. Test of portfolio efficiency
5. The generalized regression model: active portfolio management (Black-Litterman)
6. Financial returns modeling: ARMA models
7. Volatility modeling: ARCH/GARCH models

Texbooks:
Stock, J e M. Watson, Introduction to Econometrics, Pearson
Verbeek, M., A Guide to Modern Econometrics, Wiley

1. Stock-Watson, ch. 4, 5, 17

2. Stock-Watson, ch. 6, 7, 18.1-18.6

3. Suggested readings: F. Black, M. Jensen e M. Scholes (1972) “The Capital asset pricing model: some empirical tests”; E. Fama, J. MacBeth (1973), “Risk, return and equilibrium: empirical tests”, Journal of Political Economy.

4. M. Britten-Jones (1999), “The Sampling Error in Estimates of Mean-Variance Efficient Portfolio Weights”, Journal of Finance;
Suggested reading: E. Fama, K. French (1993) “Common risk factors in the returns of stocks and bonds”, Journal of Financial Economics.
For a general treatment of portfolio theory,see: Edwin J. Elton, Martin J. Gruber,Stephen J. Brown, William N. Goetzmann, Modern Portfolio Theory and Investment Analysis, Wiley and Sons.

5. Suggested readings: P. Jorion (1992) “Portfolio optimization in practice”, Financial Analyst Journal; F.Black e R.Litterman (1991) “Global portfolio optimization”, Financial Analyst Journal.

6. Verbeek, ch. 8 and handout.

7. Verbeek, ch. 8 and handout.

Examination Methods

The exam is made of one written essay and one individual homework; the final grade is given by the average of the grades in the essay and the homework, with 75% and 25% weights respectively. In order to pass the exam, it is necessary to obtain a grade not below 16/30 in the written essay.
The written essay is taken in a teaching room, lasts two hours and covers the whole program of the module.
The homework is developed individually outside the teaching rooms, and can be of two types (Homework I and Homework II). Each student can choose which type of homework to deliver, but must deliver one of them. Once the deadline for delivery of Homework II has expired, it is possible to deliver Homework I only. The homework grade remains valid throughout the academic year.

Homework I
The homework aims to develop critical skills with respect to empirical applications. Each student is free to choose one article from www.lavoce.info, www.voxeu.org/, www.ilsole24ore.com or other webiste, provided that it discusses an economic topic and makes use of data.
The homework consists in an essay of max. 2000 words, to be delivered to the address diego.lubian[at]univr.it within the day in which the latest exam of the academic year is scheduled. The homework will pass through an antiplagiarism analysis by means of the Compilation software; it is advisable to make a personal preliminary analysis before submitting the homework.
The essay must be divided in sections in such a way to contain a) a reference to the chosen article (title, authors, link), b) a summary of the article, briefly describing its motivation, goal, methodology and results, and c) a critical comment on the methodology, also proposing alternative analyses and possible future developments. The essay must also report the word count.

Homework II
The homework aims to develop analytical skills through personal data analysis in Gretl. Any student interested in this homework must write to the address diego.lubian[at]univr.it communicating name, surname and ID number. He or she will then receive a number, corresponding to the dataset to be used. The text of the homework will be made available at the end of the lectures; the solution must be delivered by email within the following three days.