The objective of this course is 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. Students at the end of the course will have a working knowledge of financial time series data and gain expertise in the software to conduct the analyses.
Prerequisiti
The course is meant to deepen the technical knowledge of the econometric mehods used in the analysis of financial markets. Neccessary prerequisites are: econometrics (the multiple linear regression model, OLS estimation, hypotheses testing, maximum likelihood estimation). Basic probability theory and stochatsic 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.
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
