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FINANCIAL ECONOMETRICS

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Financial econometrics

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Academic year 2017/2018

Course ID
SEM0087
Teacher
Luca Gambetti (Lecturer)
Degree course
Insurance and Statistics
Year
1st year
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 fi nancial time series. In particular, the course will introduce the univariate nonlinear time series models used for the analysis of financial 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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Book
Title:  
Time series analysis
Year of publication:  
1994
Publisher:  
Princeton University Press
Author:  
Hamilton, James Douglas
ISBN  
Notes:  
Ebook disponibile su piattaforma Proquest (chiedere in biblioteca)
Required:  
Yes
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Time Series Analysis, J.D. Hamilton



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