MARC record from Internet Archive

LEADER: 05645cam 2200673 i 4500
001 ocn961010441
003 OCoLC
005 20220628065750.0
008 161004s2016 txua b 001 0 eng d
010 $a 2016955738
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015 $aGBB6J1387$2bnb
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020 $a9781597182140$q(paperback)
020 $a1597182141$q(paperback)
020 $z9781597182157$q(ebook)
020 $z159718215X$q(ebook)
020 $z9781597182164$q(Mobi)
020 $z1597182168$q(Mobi)
035 $a(OCoLC)961010441
042 $alccopycat
050 00 $aHG106$b.B65 2016
082 04 $a332.015118$223
084 $a83.03$2bcl
100 1 $aBoffelli, Simona,$eauthor.
245 10 $aFinancial econometrics using Stata /$cSimona Boffelli, Giovanni Urga.
250 $aFirst edition.
264 1 $aCollege Station, Texas :$bStata Press,$c2016.
300 $axiv, 272 pages :$billustrations ;$c24 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
504 $aIncludes bibliographical references (pages 261-265) and indexes.
520 $aFinancial Econometrics Using Stata is an essential reference for graduate students, researchers, and practitioners who use Stata to perform intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models, univariate GARCH models, multivariate GARCH models, and applications of these models to financial time series. The last two chapters cover risk management and contagion measures. After a rigorous but intuitive overview, the authors illustrate each method by interpreting easily replicable Stata examples. --$cProvided by publisher.
505 0 $aMachine 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
505 0 $aNote 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
505 0 $aNote 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.
630 00 $aStata.
650 0 $aFinance$xEconometric models.
650 04 $aFinanza$xModelli econometrici$xElaborazione elettronica$xImpiego di Stata (programma per elaboratori)
650 6 $aFinances$xModèles économétriques.
630 07 $aStata.$2fast$0(OCoLC)fst01375322
650 7 $aFinance$xEconometric models.$2fast$0(OCoLC)fst00924377
650 7 $aOÌ#x88;konometrie.$2gn
610 27 $aKanton Freiburg$bAmt für Statistik$2gnd
650 7 $aÖkonometrie$2gnd
700 1 $aUrga, Giovanni,$eauthor.
938 $aBrodart$bBROD$n118091530
938 $aBaker and Taylor$bBTCP$nBK0019714648
938 $aYBP Library Services$bYANK$n13227913
029 1 $aAU@$b000059025850
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029 1 $aCHSLU$b001263297
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029 1 $aCHVBK$b48246027X
029 1 $aZWZ$b20146571X
029 1 $aUKMGB$b018114772
994 $aZ0$bIME
948 $hNO HOLDINGS IN IME - 101 OTHER HOLDINGS