27133 - Analysis of Economic and Financial Time Series

Learning outcomes

By the end of the course, students acquire the competencies to develop firm and trading strategies, based on the analysis over time of economic and financial variables. Furthermore, students will be able to make critical choices about the statistical methods for analysis and forecasting, depending on available information and the operational target. More specifically, the students will be able to: (a) describe and forecast the dynamics of firm and financial time series, through time series statistical models; (b) assess the quality of available statistical information, in order to choose the level of complexity and detail for the analysis; (c) adapt the choice of the statistical and forecasting tools to the specificities of different decision-making areas and the level of risk propensity of investors; (d) assess, also based on inferences, the results of the strategies driven by the forecasts.

Course contents

Topics in time series analysis and econometrics. The data generating process. Stochastic processes. Decomposition of time series. The classical linear multiple regression model. Spherical and non-spherical disturbances. Estimation methods. Structural breaks. Specification analysis. Heteroskedasticity. Estimation with serially correlated results. Dynamic models and lagged dependent variables.

Modelling. ARMA models and generalisation to VAR models. Cointegration. ARCH, GARCH and stochastic volatility models. Structural time series model and Kalman filtering.

Data. Datastream for financial and economic time series. ISTAT and OECD time series.

Financial applications. Event Study Analysis. Volatility models: ARCH, GARCH, and stochastic volatility models.

Economic applications. Structural time series models. Time-varying coefficients in the regression model. .

Readings/Bibliography

Lecture notes supplied by the lecturer and available before and during the course on the AMS Campus platform.

W.H. Greene. Econometric Analysis. Prentice Hall (7th edition or any of the previous ones).

Teaching methods

The course is based on front lectures and frequent tutorials in the IT Lab, focusing on econometric estimation and forecasting  using Econometric Views software.

Assessment methods

Oral exam. Students will be also required to present – by the end of the course – an essay on the econometric estimation of a financial or economic model among those introduced in the course (details will be supplied during the tutorials).

Teaching tools

Econometric Views (through the UbiqueLab facility)

Links to further information

http://www2.stat.unibo.it/mazzocchi

Office hours

See the website of Mario Mazzocchi