Financial Econometrics Using Stata
Financial Econometrics Using Stata is an essential reference for graduate students, researchers, and practitioners who use Stata to perform intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models, univariate GARCH models, multivariate GARCH models, and applications of these models to financial time series. The last two chapters cover risk management and contagion measures. After a rigorous but intuitive overview, the authors illustrate each method by interpreting easily replicable Stata examples.
Table of Contents
Introduction to financial time series
The object of interest
Approaching the dataset
Linear time series
How to import data
Autoregressive (AR) processes
Moving-average (MA) processes
Autoregressive moving-average (ARMA) processes
Application of ARMA models
Modeling volatilities, ARCH models, and GARCH models
Asymmetric GARCH models
Alternative GARCH models
Multivariate GARCH models
Direct generalizations of the univariate GARCH model of Bollerslev
Nonlinear combination of univariate GARCH—common features
Simona Boffelli, PhD, is a quantitative analyst at Fineco Bank in Milan, part of the Unicredit Group. She is a researcher associate to the Department of Management, Economics and Quantitative Methods of Bergamo University in Italy and to the Centre for Econometric Analysis of Cass Business School in London.
Giovanni Urga, PhD, is a professor of finance and econometrics and the director of the Centre for Econometric Analysis at Cass Business School in London, and is a professor of econometrics at the Department of Management, Economics and Quantitative Methods of Bergamo University in Italy.