ID:
504838
Durata (ore):
66
CFU:
9
SSD:
ECONOMETRIA
Anno:
2024
Dati Generali
Periodo di attività
Primo Semestre (23/09/2024 - 20/12/2024)
Syllabus
Obiettivi Formativi
This course aims to provide a comprehensive and systematic account of financial econometric models and their applications to modeling and prediction of financial time series data, focusing on asset returns and volatilities. The students will learn the analytical tools needed for the specification and estimation of econometric models with financial data. At the end of the course, students will have a working knowledge of financial time series data and gain expertise in the software used to conduct the analyses.
Prerequisiti
The course is meant to deepen the technical knowledge of the econometric methods used in the analysis of financial markets. Necessary prerequisites are:
econometrics (the multiple linear regression model, OLS estimation, hypotheses testing, maximum likelihood estimation). Basic probability theory and stochastic calculus.
econometrics (the multiple linear regression model, OLS estimation, hypotheses testing, maximum likelihood estimation). Basic probability theory and stochastic calculus.
Metodi didattici
In-person classes
Verifica Apprendimento
The examination is based on a written test. In the written test the students are requested to solve exercises and to answer short theory questions. Books and calculators will not be allowed in the exam.
Testi
Ait-Sahalia J.Jacod (2014) High-Frequency Financial Econometrics
Princeton University Press
Hamilton J. (1994), Time Series Analysis, Princenton University Press.
Taylor S.J. (2005) Asset Prices Dynamics, volatility, and prediction, Princenton University Press.
Singleton K. (2006) Empirical Dynamic Asset Pricing, Princenton University Press.
Princeton University Press
Hamilton J. (1994), Time Series Analysis, Princenton University Press.
Taylor S.J. (2005) Asset Prices Dynamics, volatility, and prediction, Princenton University Press.
Singleton K. (2006) Empirical Dynamic Asset Pricing, Princenton University Press.
Contenuti
1.Finite difference equations. Solutions and stability. Stationarity and
ergodicity
2. ARMA models: Stationarity, invertibility, forecasting
3. Maximum likelihood estimation of ARMA models
4. VAR: representation and estimation
5. Stochastic trends and deterministic trends. Unit root testing
2. Empirical asset pricing models: Generalized method of moments (GMM)
3. Volatility of financial returns: models, estimation, forecasting
(a) Introduction
(b) Univariate GARCH models (T, 8,9,10)
(c) Multivariate GARCH models
(d) Stochastic volatility models
(e) Nonparametric estimation of volatility with high-frequency data
ergodicity
2. ARMA models: Stationarity, invertibility, forecasting
3. Maximum likelihood estimation of ARMA models
4. VAR: representation and estimation
5. Stochastic trends and deterministic trends. Unit root testing
2. Empirical asset pricing models: Generalized method of moments (GMM)
3. Volatility of financial returns: models, estimation, forecasting
(a) Introduction
(b) Univariate GARCH models (T, 8,9,10)
(c) Multivariate GARCH models
(d) Stochastic volatility models
(e) Nonparametric estimation of volatility with high-frequency data
Lingua Insegnamento
INGLESE
Altre informazioni
Time series methods:
1. Finite difference equations. Solutions and stability. Stationarity and
ergodicity (H, 1,2)
2. ARMA models: Stationarity, invertibility, forecasting (H, 3.1, 3.2,
3.3, 3.4, 3.5, 3.7, 4.1, 4.2, 4.3)
3. Maximum likelihood estimation of ARMA models (H, 5)
4. Stochastic trends and deterministic trends (H, 15.1, 15.2, 15.3, 15.4).
5. Unit root tests (H, 17.1,17.2,17.3,17.4,17.5,17.6)
6. VAR: representation and estimation (H, 10.1, 10.2, 11.1).
7. The Generalized method of moments (Hall, 1, 2, 3. H 13.1-13.21)
8. Cointegration and cointegrated VAR (J, 3)
• Volatility of financial returns: models, estimation, forecasting
1. Stylized Facts
2. Univariate GARCH models. (RS)
3. Multivariate GARCH models. (RS)
4. Stochastic volatility models. (T, 11)
5. Nonparametric estimation of volatility with high-frequency data: the
realized variance. (ABD)
• An introduction to factor models Prof. Lorenzo Trapani
References
Hansen B. (2022), Econometrics, Princenton University Press. (B)
Davidson J. (2000), Econometric Theory, Blackwell. (D)
A. R. Hall (2005), Generalized Method of Moments, Cambridge University Press.
(Hall)
Hamilton J. (1994), Time Series Analysis, Princenton University Press. (H)
E. Rossi and F.Spazzini “GARCH models for commodity markets” (2015) in
Andrea Roncoroni, Gianluca Fusai, Mark Cummins (Eds) Handbook of Multi-Commodity Markets and Products: Structuring, Trading and Risk Management. John Wiley and Sons. ISBN: 978-0-470-74524-3. (RS)
Torben G. Andersen, Tim Bollerslev, and Francis X. Diebold, “Parametric and
Nonparametric Volatility Measurement” Handbook of Financial Econometrics
Volume 1: Tools and Techniques (pp. 67-137). North-Holland. Oxford
University Press. (ABD)
Johansen, S. (1996), Likelihood Based Inference on Cointegration in the Vector
Autoregressive Model, Oxford University Press. (J)
Taylor S.J. (2005) Asset Prices Dynamics, volatility, and prediction, Princenton
University Press. (T)
1. Finite difference equations. Solutions and stability. Stationarity and
ergodicity (H, 1,2)
2. ARMA models: Stationarity, invertibility, forecasting (H, 3.1, 3.2,
3.3, 3.4, 3.5, 3.7, 4.1, 4.2, 4.3)
3. Maximum likelihood estimation of ARMA models (H, 5)
4. Stochastic trends and deterministic trends (H, 15.1, 15.2, 15.3, 15.4).
5. Unit root tests (H, 17.1,17.2,17.3,17.4,17.5,17.6)
6. VAR: representation and estimation (H, 10.1, 10.2, 11.1).
7. The Generalized method of moments (Hall, 1, 2, 3. H 13.1-13.21)
8. Cointegration and cointegrated VAR (J, 3)
• Volatility of financial returns: models, estimation, forecasting
1. Stylized Facts
2. Univariate GARCH models. (RS)
3. Multivariate GARCH models. (RS)
4. Stochastic volatility models. (T, 11)
5. Nonparametric estimation of volatility with high-frequency data: the
realized variance. (ABD)
• An introduction to factor models Prof. Lorenzo Trapani
References
Hansen B. (2022), Econometrics, Princenton University Press. (B)
Davidson J. (2000), Econometric Theory, Blackwell. (D)
A. R. Hall (2005), Generalized Method of Moments, Cambridge University Press.
(Hall)
Hamilton J. (1994), Time Series Analysis, Princenton University Press. (H)
E. Rossi and F.Spazzini “GARCH models for commodity markets” (2015) in
Andrea Roncoroni, Gianluca Fusai, Mark Cummins (Eds) Handbook of Multi-Commodity Markets and Products: Structuring, Trading and Risk Management. John Wiley and Sons. ISBN: 978-0-470-74524-3. (RS)
Torben G. Andersen, Tim Bollerslev, and Francis X. Diebold, “Parametric and
Nonparametric Volatility Measurement” Handbook of Financial Econometrics
Volume 1: Tools and Techniques (pp. 67-137). North-Holland. Oxford
University Press. (ABD)
Johansen, S. (1996), Likelihood Based Inference on Cointegration in the Vector
Autoregressive Model, Oxford University Press. (J)
Taylor S.J. (2005) Asset Prices Dynamics, volatility, and prediction, Princenton
University Press. (T)
Corsi
Corsi
FINANCE
Laurea Magistrale
2 anni
No Results Found
Persone
Persone (3)
No Results Found