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.

Introduction to financial time series

The object of interest

Approaching the dataset

Normality

Stationarity

Autocorrelation

Heteroskedasticity

Linear time series

Model selection

How to import data

Autoregressive (AR) processes

Moving-average (MA) processes

Autoregressive moving-average (ARMA) processes

Application of ARMA models

Introduction

ARCH models

ARCH(p)

GARCH models

Asymmetric GARCH models

Alternative GARCH models

Introduction

Multivariate GARCH

Direct generalizations of the univariate GARCH model of Bollerslev

Nonlinear combination of univariate GARCH—common features

Final remarks

Introduction

Loss

Risk measures

VaR

Backtesting procedures

Introduction

Contagion measurement

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.