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FINANCIAL ECONOMETRICS
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Financial econometrics
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Academic year 2018/2019
- Course ID
- SEM0087
- Teacher
- Luca Gambetti (Lecturer)
- Degree course
- Finance
- Year
- 1st year
- Teaching period
- Second semester
- Type
- Related or integrative
- Credits/Recognition
- 6
- Course disciplinary sector (SSD)
- SECS-P/05 - econometria
- Delivery
- Formal authority
- Language
- English
- Attendance
- Optional
- Type of examination
- Oral
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Sommario del corso
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Course objectives
This course provides an introduction to the econometric techniques used for the analysis of economic and financial time series. The course begins with an overview of ARMA and VARMA models, and then focuses on the nonlinear time series model used for the analysis of nancial time series. In particular, the course will introduce the univariate nonlinear time series models used for the analysis of nancial volatility (GARCH models) and the multivariate nonlinear models for the analysis of correlations (DCC models).
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Results of learning outcomes
Students are expected to learn how to use the economeetric models discussed and to conduct individually analyses of economic and financial data.
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Course delivery
The course consists of 48 lecture hours. Strong interaction between teachers and students is encouraged. Part of the course will be given at the Computer Lab.
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Learning assessment methods
- problem sets
- written final exam 2 h. and 30 m. (max.) at the end of the course
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Support activities
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Program
0. User's Guide
1. Intro to Times Series
1.1. Time Series in Economics and Finance
1.2. Time Series as Stochastic Processes
1.3. Time Series Properties
2. Univariate Linear Time Series (ARMA)
2.1. Linear Time Series: Models
2.2. Linear Time Series: Prediction
2.2 Linear Time Series: Estimation
2.4. Linear Time Series: Practice
3. Multivariate Linear Time Series (VARMA)
3.1. Linear Time Series: Models
3.2. Linear Time Series: Prediction
3.3. Linear Time Series: Estimation
3.4. Linear Time Series: Practice
4. Volatility Modeling
4.1. Volatility Modeling: ARCH and GARCH
4.2. Volatility Modeling: Asymmetric Effects
4.3. Volatility Modeling: Prediction and Evalution
4.4. *Volatility Modeling: Stochastic Volatility
4.5. *Volatility Modeling: High Frequency Data Based Volatility Modelling
5. Covariance Modeling
5.1. Multivariate Volatility Models
6. Nonlinear Time Series Models
6.1 TVAR
6.2 STVAR
6.3 TVC-VAR
Suggested readings and bibliography
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Time Series Analysis, J.D. Hamilton
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Class schedule
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