Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to time-series analysis and how to do it in Stata for financial economists. Aimed at researchers, graduate students, and industry practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results.
After providing an intuitive introduction to time-series analysis and the ubiquitous autoregressive moving average (ARMA) model, the authors carefully cover univariate and multivariate models for volatilities. Chapters on risk management and analyzing contagion show how to define, estimate, interpret, and perform inference on essential measures of risk and contagion.
The authors illustrate every topic with easily replicable Stata examples and explain how to interpret the results from these examples.
The authors have a unique blend of academic and industry training and experience. This training produced a practical and thorough approach to each of the addressed topics.
© Copyright 1996–2023 StataCorp LLC
List of figures
Preface (PDF)
Notation and typography
1.2 Approaching the dataset
1.3 Normality
1.4 Stationarity
1.5 Autocorrelation
1.5.2 PACF
1.6 Heteroskedasticity
1.7 Linear time series
1.8 Model selection
1.A How to import data
2.1 Autoregressive (AR) processes
2.1.2 AR(p)
2.2 Moving-average (MA) processes
2.2.2 MA(q)
2.2.3 Invertibility
2.3 Autoregressive moving-average (ARMA) processes
2.3.2 ARMA(p,q)
2.3.3 ARIMA
2.3.4 ARMAX
2.4 Application of ARMA models
2.4.2 Postestimation
2.4.3 Adding a dummy variable
2.4.4 Forecasting
3.2 ARCH models
3.2.1 General options
Distribution
3.2.2 Additional options
The het() option
The maximize_options options
3.2.3 Postestimation
3.3 ARCH(p)
3.4 GARCH models
3.4.2 GARCH in mean
3.4.3 Forecasting
3.5 Asymmetric GARCH models
3.5.2 TGARCH
3.5.3 GJR–GARCH
3.5.4 APARCH
3.5.5 News impact curve
3.5.6 Forecasting comparison
3.6 Alternative GARCH models
3.6.2 NGARCH
3.6.3 NGARCHK
4.2 Multivariate GARCH
4.3 Direct generalizations of the univariate GARCH model of Bollerslev
4.3.2 Diagonal vech model
4.3.3 BEKK model
4.3.4 Empirical application
Dvech model
4.4 Nonlinear combination of univariate GARCH—common features
4.4.1 Constant conditional correlation (CCC) GARCH
4.4.2 Dynamic conditional correlation (DCC) model
Empirical application
Dynamic conditional correlation Tse and Tsui (DCCT)
Prediction
4.5 Final remarks
5.2 Loss
5.3 Risk measures
5.4 VaR
5.4.2 Parametric approach
5.4.3 Historical simulation
5.4.4 Monte Carlo simulation
5.4.5 Expected shortfall
5.5 Backtesting procedures
5.5.1 Unilevel VaR tests
The independence test
The conditional coverage test
The duration tests
6.2 Contagion measurement
6.2.1 Cross-market correlation coefficients
6.2.2 ARCH and GARCH models
Markov switching
6.2.3 Higher moments contagion
Glossary of acronyms
References
© Copyright 1996–2023 StataCorp LLC
