69830 - Financial Econometrics A.C.

Learning outcomes

At the end of the course the student deals with the economeric analysis of the main models of asset pricing and the univariate amd multivariate time-series models typically used in finance.
The use of econometric packages and applied work is encouraged.

Course contents

The course is divided into two parts.

Part one. Basic facts about risk measures based on conditional volatility models. Baseline time series models.

Part two. Solid background for the econometric relatinships in finance: from OLS to IV regressions and GMM estimation of main asset price models. 

 

Part one

Prices and returns of financial assets: definitions

Stationary time series and laws of large numbers.

Three stylized facts on asset returns and a proposed model for modelling asset returns volatility.

ARCH and GARCH models and their use in quantitative finance.

 

Part two

The classic and generalized linear regression models.

The CAPM as a regression model with conditional heteroskedasticity.

Estimation issues and main diagnostic analysis

Endogeneity issues: IV estimation.

GMM estimation of Present Value models and the C-CAPM

Readings/Bibliography

Teaching material is provided by the teachar in form of slides. The student can further refer to the following books:

CAMPBELL, J.Y., LO, A.W., MacKINLAY (1997), The econometrics of financial markets, Princeton University Press.

TSAY (2002) Analysis of Financial Time Series, Wiley.

Verbeek, M. (2000), Modern Econometrics, Wiley.

Palomba, G. (2010), Elementi di statistica per l'econometria", Clua Ancona. This text is recommended for students who do not posses a "complete" background in econometrics.

Teaching methods

Theoretical lessons and empirical cases studied in the classroom

Assessment methods

The ultimate goal of the exam is to verify that the students have achieved the following objectives:

• the knowledge of basic econometric models and their application to the special features which characterize financial markets;

• the knowledge of OLS, GLS and IV estimators, their properties and application fields;

• the knowledge of main time-series models of the ARIMA class, especially their use for forecasting purposes;

• the knowledge of the class of ARCH and GARCH models, their use to dynamic risk evaluation and for forecasting purposes.

The exam is written and a final grade like xx/30 is given.

The student is strongly encouraged and motivated to take a partial written exam at the end of the first part. Those who take the first partial will only take the topics related to the second part in the first official exam (and exclusively in this one).

Students are supposed to both theoretical and practical exercises. Real cases can be also discussed.

Artificial Intelligence (AI) may be used as a valuable tool to support independent study by providing additional explanations, summaries, and self-assessment activities. However, during examinations, the use of AI is strictly prohibited. Any use of AI during the examination constitutes a violation of academic integrity

 

Teaching tools

The main package used is Gretl an open-source software that the student can download for free

Links to further information

https://www.unibo.it/sitoweb/luca.fanelli

Office hours

See the website of Luca Fanelli