ID:
504838
Durata (ore):
66
CFU:
9
SSD:
ECONOMETRIA
Anno:
2025
Dati Generali
Periodo di attività
Primo Semestre (22/09/2025 - 19/12/2025)
Syllabus
Obiettivi Formativi
This course offers a comprehensive and systematic exploration of financial econometric models, emphasizing their application in modeling and forecasting financial time series data, particularly asset returns and volatilities. Students will acquire the essential analytical tools for specifying and estimating econometric models using financial data. By the end of the course, participants will possess a solid understanding of financial time series necessary for conducting rigorous 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. Ahn, S. C. and A. R. Horenstein (2013). Eigenvalue ratio test for the number of factors. Econometrica 81, 1203–1227. Bai, J. and S. Ng (2002). Determining the number of factors in approximate factor models. Econometrica 70(1), 191--221. Bai, J. (2003). Inferential theory for factor models of large dimensions. Econometrica 71(1), 135--171. Bai, J. (2004). Estimating cross-section common stochastic trends in nonstationary panel data. Journal of Econometrics 122, 137–183. Giglio, S. and D. Xiu (2021). Asset pricing with omitted factors. Journal of Political Economy 129, 1947–1990. Massacci, D., L. Sarno, and L. Trapani (2025). Factor models of asset returns and bear market risk. Management Science, forthcoming. Trapani, L. (2018). A randomized sequential procedure to determine the number of factors. Journal of the American Statistical Association 113, 1341–1349.
Contenuti
1. Finite difference equations. Solutions and stability. Stationarity and ergodicity 2. ARMA models: Stationarity, invertibility, forecasting, estimation 4. VAR: representation and estimation 5. Stochastic trends and deterministic trends. Unit root testing 6. Empirical asset pricing models: Generalized method of moments (GMM) 7. Volatility of financial returns: Parametric models; Nonparametric estimation of volatility with high-frequency data. 8. Factor models and asset pricing applications.
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