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Financial econometrics using Stata

Author: Simona Boffelli; Giovanni Urga
Publisher: College Station, Texas : Stata Press, 2016.
Edition/Format:   Print book : English : First editionView all editions and formats
Summary:
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  Read more...
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Document Type: Book
All Authors / Contributors: Simona Boffelli; Giovanni Urga
ISBN: 9781597182140 1597182141
OCLC Number: 961010441
Description: xiv, 272 pages : illustrations ; 24 cm
Contents: Machine generated contents note: 1.Introduction to financial time series --
1.1.The object of interest --
1.2.Approaching the dataset --
1.3.Normality --
1.4.Stationarity --
1.4.1.Stationarity tests --
1.5.Autocorrelation --
1.5.1.ACF --
1.5.2.PACF --
1.6.Heteroskedasticity --
1.7.Linear time series --
1.8.Model selection --
1.A.How to import data --
2.ARMA models --
2.1.Autoregressive (AR) processes --
2.1.1.AR(1) --
2.1.2.AR(p) --
2.2.Moving-average (MA) processes --
2.2.1.MA(1) --
2.2.2.MA(q) --
2.2.3.Invertibility --
2.3.Autoregressive moving-average (ARMA) processes --
2.3.1.ARMA(1,1) --
2.3.2.ARMA(p,q) --
2.3.3.ARIMA --
2.3.4.Armax --
2.4.Application of ARMA models --
2.4.1.Model estimation --
2.4.2.Postestimation --
2.4.3.Adding a dummy variable --
2.4.4.Forecasting --
3.Modeling volatilities, ARCH models, and GARCH models --
3.1.Introduction --
3.2.ARCH models --
3.2.1.General options --
ARCH --
Distribution --
3.2.2.Additional options --
ARIMA --
The het() option Note continued: The maximize_options options --
3.2.3.Postestimation --
3.3.ARCH(p) --
3.4.GARCH models --
3.4.1.GARCH(p,q) --
3.4.2.GARCH in mean --
3.4.3.Forecasting --
3.5.Asymmetric GARCH models --
3.5.1.SAARCH --
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 --
3.6.1.PARCH --
3.6.2.NGARCH --
3.6.3.NGARCHK --
4.Multivariate GARCH models --
4.1.Introduction --
4.2.Multivariate GARCH --
4.3.Direct generalizations of the univariate GARCH model of Bollerslev --
4.3.1.Vech model --
4.3.2.Diagonal vech model --
4.3.3.BEKK model --
4.3.4.Empirical application --
Data description --
Dvech model --
4.4.Nonlinear combination of univariate GARCH --
common features --
4.4.1.Constant conditional correlation (CCC) GARCH --
Empirical application --
4.4.2.Dynamic conditional correlation (DCC) model --
Dynamic conditional correlation Engle (DCCE) model --
Empirical application Note continued: Dynamic conditional correlation Tse and Tsui (DCCT) --
Prediction --
4.5.Final remarks --
5.Risk management --
5.1.Introduction --
5.2.Loss --
5.3.Risk measures --
5.4.VaR --
5.4.1.VaR estimation --
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 unconditional coverage test --
The independence test --
The conditional coverage test --
The duration tests --
6.Contagion analysis --
6.1.Introduction --
6.2.Contagion measurement --
6.2.1.Cross-market correlation coefficients --
Empirical exercise --
6.2.2.ARCH and GARCH models --
Empirical exercise --
Markov switching --
6.2.3.Higher moments contagion --
Empirical exercise.
Responsibility: Simona Boffelli, Giovanni Urga.

Abstract:

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. --

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   schema:bookEdition "First edition." ;
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   schema:description "Note continued: Dynamic conditional correlation Tse and Tsui (DCCT) -- Prediction -- 4.5.Final remarks -- 5.Risk management -- 5.1.Introduction -- 5.2.Loss -- 5.3.Risk measures -- 5.4.VaR -- 5.4.1.VaR estimation -- 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 unconditional coverage test -- The independence test -- The conditional coverage test -- The duration tests -- 6.Contagion analysis -- 6.1.Introduction -- 6.2.Contagion measurement -- 6.2.1.Cross-market correlation coefficients -- Empirical exercise -- 6.2.2.ARCH and GARCH models -- Empirical exercise -- Markov switching -- 6.2.3.Higher moments contagion -- Empirical exercise."@en ;
   schema:description "Machine generated contents note: 1.Introduction to financial time series -- 1.1.The object of interest -- 1.2.Approaching the dataset -- 1.3.Normality -- 1.4.Stationarity -- 1.4.1.Stationarity tests -- 1.5.Autocorrelation -- 1.5.1.ACF -- 1.5.2.PACF -- 1.6.Heteroskedasticity -- 1.7.Linear time series -- 1.8.Model selection -- 1.A.How to import data -- 2.ARMA models -- 2.1.Autoregressive (AR) processes -- 2.1.1.AR(1) -- 2.1.2.AR(p) -- 2.2.Moving-average (MA) processes -- 2.2.1.MA(1) -- 2.2.2.MA(q) -- 2.2.3.Invertibility -- 2.3.Autoregressive moving-average (ARMA) processes -- 2.3.1.ARMA(1,1) -- 2.3.2.ARMA(p,q) -- 2.3.3.ARIMA -- 2.3.4.Armax -- 2.4.Application of ARMA models -- 2.4.1.Model estimation -- 2.4.2.Postestimation -- 2.4.3.Adding a dummy variable -- 2.4.4.Forecasting -- 3.Modeling volatilities, ARCH models, and GARCH models -- 3.1.Introduction -- 3.2.ARCH models -- 3.2.1.General options -- ARCH -- Distribution -- 3.2.2.Additional options -- ARIMA -- The het() option"@en ;
   schema:description "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. --"@en ;
   schema:description "Note continued: The maximize_options options -- 3.2.3.Postestimation -- 3.3.ARCH(p) -- 3.4.GARCH models -- 3.4.1.GARCH(p,q) -- 3.4.2.GARCH in mean -- 3.4.3.Forecasting -- 3.5.Asymmetric GARCH models -- 3.5.1.SAARCH -- 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 -- 3.6.1.PARCH -- 3.6.2.NGARCH -- 3.6.3.NGARCHK -- 4.Multivariate GARCH models -- 4.1.Introduction -- 4.2.Multivariate GARCH -- 4.3.Direct generalizations of the univariate GARCH model of Bollerslev -- 4.3.1.Vech model -- 4.3.2.Diagonal vech model -- 4.3.3.BEKK model -- 4.3.4.Empirical application -- Data description -- Dvech model -- 4.4.Nonlinear combination of univariate GARCH -- common features -- 4.4.1.Constant conditional correlation (CCC) GARCH -- Empirical application -- 4.4.2.Dynamic conditional correlation (DCC) model -- Dynamic conditional correlation Engle (DCCE) model -- Empirical application"@en ;
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