## FINANCIAL ECONOMETRICS

## Syllabus

EN
IT

### Learning Objectives

LEARNING OUTCOMES:

Econometric models of financial markets form integral part of the curriculum in economics and finance.

The course deals with the measurement, analysis and prediction of market risk. A core component is modelling volatility via conditional heteroscedastic models, i.e. ARCH and GARCH models and their extensions. We will consider their mutlivariate extensions and their role for portfolio management, touching upon high dimensional methods for financial time series and the theory of copulae. The class of stochastic volatility models will be considered and finally, we will devote our attention to the prediction of realized volatility using long memory models.

Matlab and R illustrations for integral part of the course.

KNOWLEDGE AND UNDERSTANDING:

The course teaches essential methods for predicting volatility. It provides a solid theoretical background on econometric methods for the analysis of financial markets.

APPLYING KNOWLEDGE AND UNDERSTANDING:

The methodologies exposed during the course are applied to real life datasets and case studies, dealing with the prediction of the volatility.

Two hours per week are dedicated to tutorials where statistical analyses are conducted in the Laboratory and implemented in Matlab and R-studio.

Students are expected to perform their statistical analyses in weekly assignments

MAKING JUDGEMENTS:

The prediction of an outcome is an informed decision based on the knowledge of covariates and antecedents. The student is expected to be able to draw conclusions on the basis of the statistical evidence and to validate those conclusions on validation or test samples drawn from the same target population.

COMMUNICATION SKILLS:

Particular attention is dedicated to the ability to communicate the statistical evidence in a systematic and synthetic way, using graphs and summaries, to a non-specialist target audience.

The software used in the tutorials is oriented towards graphical displays and visualization of data. The student is asked to report on the statistical analysis carried out for a particular purpose in the individual assignments.

LEARNING SKILLS:

Students develop their learning skills by comparing the teaching material provided by the instructor and exposed in the lectures with the readings suggested with weekly periodicity. The software tutorials and the analysis of cases studies in the assignments will help build their applied skills and their autonomous progress towards the intended learning outcomes.

Econometric models of financial markets form integral part of the curriculum in economics and finance.

The course deals with the measurement, analysis and prediction of market risk. A core component is modelling volatility via conditional heteroscedastic models, i.e. ARCH and GARCH models and their extensions. We will consider their mutlivariate extensions and their role for portfolio management, touching upon high dimensional methods for financial time series and the theory of copulae. The class of stochastic volatility models will be considered and finally, we will devote our attention to the prediction of realized volatility using long memory models.

Matlab and R illustrations for integral part of the course.

KNOWLEDGE AND UNDERSTANDING:

The course teaches essential methods for predicting volatility. It provides a solid theoretical background on econometric methods for the analysis of financial markets.

APPLYING KNOWLEDGE AND UNDERSTANDING:

The methodologies exposed during the course are applied to real life datasets and case studies, dealing with the prediction of the volatility.

Two hours per week are dedicated to tutorials where statistical analyses are conducted in the Laboratory and implemented in Matlab and R-studio.

Students are expected to perform their statistical analyses in weekly assignments

MAKING JUDGEMENTS:

The prediction of an outcome is an informed decision based on the knowledge of covariates and antecedents. The student is expected to be able to draw conclusions on the basis of the statistical evidence and to validate those conclusions on validation or test samples drawn from the same target population.

COMMUNICATION SKILLS:

Particular attention is dedicated to the ability to communicate the statistical evidence in a systematic and synthetic way, using graphs and summaries, to a non-specialist target audience.

The software used in the tutorials is oriented towards graphical displays and visualization of data. The student is asked to report on the statistical analysis carried out for a particular purpose in the individual assignments.

LEARNING SKILLS:

Students develop their learning skills by comparing the teaching material provided by the instructor and exposed in the lectures with the readings suggested with weekly periodicity. The software tutorials and the analysis of cases studies in the assignments will help build their applied skills and their autonomous progress towards the intended learning outcomes.

### Prerequisites

Introductory courses in statistics and econometrics (time series)

### Program

1 Introduction. Asset returns. Stylized facts: asymmetry, kurtosis and volatility clustering. Stochastic processes: stationarity, purely random processes. Random walks and martingales. Review of prediction theory. Optimal prediction.

2 Volatility measurement and analysis: autoregressive Conditional Heteroscedasticity (ARCH): model specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean. Generalized ARCH models, Integrated GARCH, Exponential GARCH models. GJR-GARCH, Leverage. Fat and heavy tails.

3 Multivariate GARCH models. VEC and BEKK. Conditional correlation models: constant and dynamic, CCC, DCC. Factor models: Factor GARCH, O-GARCH. Large dimensional covariance and correlation matrices.

4 Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter.

5 Realized volatility. Long memory.

2 Volatility measurement and analysis: autoregressive Conditional Heteroscedasticity (ARCH): model specification, properties, maximum likelihood estimation, prediction. Extensions: ARCH in mean. Generalized ARCH models, Integrated GARCH, Exponential GARCH models. GJR-GARCH, Leverage. Fat and heavy tails.

3 Multivariate GARCH models. VEC and BEKK. Conditional correlation models: constant and dynamic, CCC, DCC. Factor models: Factor GARCH, O-GARCH. Large dimensional covariance and correlation matrices.

4 Stochastic volatility models. Pseudo-maximum likelihood inference. State space models. The Kalman filter.

5 Realized volatility. Long memory.

### Books

Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.

Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.

Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.

Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.

McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.

Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.

Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.

Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.

Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.

McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.

Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.

### Bibliography

Campbell, J., Lo, A. and MacKinlay, A. (1999). The Econometrics of Financial Markets. Princeton University Press: New Jersey.

Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.

Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.

Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.

McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.

Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.

Fan J. and Yao, Q. (2017). The Elements of Financial Econometrics. Cambride University Press.

Franke, J., Haerdle, W.K. and Hafner, C.M. (2012). Statistics of Financial Markets. An Introduction. Third Edition. Springer.

Linton O. (2019). Financial Econometrics: Models and Methods. Cambridge University Press.

McNeil, A.J., Frey, R. and Embrechts, P. (2005). Quantitative Risk Management, Princeton Series in Finance.

Taylor, S. J. (2005). Asset Price Dynamics, Volatility, and Prediction. Princeton University Press.

### Teaching methods

Lectures, slides presentation, use of blackboard.

### Exam Rules

• Lectures (36 hours)

• Tutorials (12 hours)

Assessment for this course has two components

30% Four Weekly Assignments

70% Final Exam

The final exam is a written test

The weekly work deals with the elaboration and presentation of case studies concerning the prediction of the work of the volatility and value at risk of one or more financial securities. It aims to course the ability to work in a team, to use the knowledge and methodologies acquired while communicating, as well as the statistical evidence.

The final exam is a 90-minute written test that evaluates the learning of the program topics.

• Tutorials (12 hours)

Assessment for this course has two components

30% Four Weekly Assignments

70% Final Exam

The final exam is a written test

The weekly work deals with the elaboration and presentation of case studies concerning the prediction of the work of the volatility and value at risk of one or more financial securities. It aims to course the ability to work in a team, to use the knowledge and methodologies acquired while communicating, as well as the statistical evidence.

The final exam is a 90-minute written test that evaluates the learning of the program topics.