AIMS AND CONTENT

LEARNING OUTCOMES

The course provides a survey of the theory and application of time series models in financial econometrics. Students are introduced to time series analysis of linear univariate and multivariate covariance stationary models with short and long memory parameterization. The course then employs linear time series knowledge to introduce studen ts to time series financial econometrics models, particularly discrete-time parametric ARCH models. The main objective of this course is to develop the skills needed for modelling and forecasting assets volatilities and their co-movements in financial markets. The course aims to provide students with a strong theoretical understanding of volatility models and techniques for estimations, assessment and forecasting in financial markets under a variety of degree of shock persistence. Theoretical lectures are complemented by computer classes whose aim is to enable the students to develop computational skills in MATLAB for empirical research

AIMS AND LEARNING OUTCOMES

The course is designed to introduce the econometric tools used in in time series analysis and finance, and to gain understanding of the sources and characteristic of financial data as well as current and classic applications. The interaction between theory and empirical analysis is emphasised. Students are introduced to time series analysis of linear univariate and multivariate covariance stationary models with short and long memory parameterization. Llinear time series knowledge is employed to introduce students to time series financial econometrics models, particularly discrete- time parametric ARCH models.. The course aims to provide students with a strong theoretical understanding of volatility models and techniques for estimations, assessment and forecasting in financial markets under a variety of degree of shock persistence. 

SYLLABUS/CONTENT

TOPIC I: LINEAR TIME SERIES ANALYSIS .

• Stochastic processes, covariance stationarity, strict stationarity, unit root processes, fractionally integrated processes, Wold decomposition theorem.
• AR, MA, ARMA,ARIMA,ARFIMA univariate models: estimation and principles of forecasting.
• Unit root tests,long memory tests, cointegration,model diagnostic.

TOPIC II: UNIVARIATE GARCH MODELS.

• Introduction of asset returns
• ARCH model: identification and covariance stationarity conditions ,order identification, estimation, evaluation
• GARCH model: identification and covariance stationarity conditions ,order identification, estimation, evaluation and forecasting.
• Asymmetric GARCH models and leverage effects:EGARCH,QGARCH,GJGARCH,TGARCH: identification and covariance stationarity conditions ,order identification, estimation, evaluation and forecasting.
• Long memory in univariate GARCH models: testing for long memory in the time series domain, forecasting in presence of long memory.

TOPIC  III: VAR MODELS.

• Introduction to VAR models: properties and characteristics
• Econometric approach to VAR and estimation

TOPIC  IV: MULTIVARIATE GARCH MODELS.

• Introduction to Multivariate GARCH MODELS
• Co-movements of financial returns: empirical and theoretical examples. Introduction to MGARCH models and specific issues.
• FACTOR MODELS
• NON PARAMETRIC models
• Testing in MGARCH models

RECOMMENDED READING/BIBLIOGRAPHY

Hamilton "Time series econometrics"

Franq Zaquoian "Garch models"

additional reading will be raccomanded during the course

TEACHERS AND EXAM BOARD

Exam Board

GABRIELE DEANA (President)

ANNA BOTTASSO

LESSONS

Class schedule

FINANCIAL ECONOMETRICS

EXAMS

EXAM DESCRIPTION

written

Exam schedule