LEADER 05795nam 2200769Ia 450 001 9910141294203321 005 20200520144314.0 010 $a1-280-59147-1 010 $a9786613621306 010 $a1-118-27205-6 010 $a1-118-27203-X 010 $a1-118-27199-8 035 $a(CKB)2670000000167332 035 $a(EBL)836590 035 $a(SSID)ssj0000636247 035 $a(PQKBManifestationID)11420825 035 $a(PQKBTitleCode)TC0000636247 035 $a(PQKBWorkID)10660370 035 $a(PQKB)11610114 035 $a(Au-PeEL)EBL836590 035 $a(CaPaEBR)ebr10546576 035 $a(CaONFJC)MIL362130 035 $a(PPN)170610829 035 $a(FR-PaCSA)88813016 035 $a(MiAaPQ)EBC836590 035 $a(OCoLC)785426815 035 $a(EXLCZ)992670000000167332 100 $a20111130d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHandbook of volatility models and their applications /$fLuc Bauwens, Christian Hafner, Sebastien Laurent 205 $a1st ed. 210 $aHoboken, N.J. $cJohn Wiley & Sons, Inc.$d2012 215 $a1 online resource (565 p.) 225 1 $aWiley handbooks in financial engineering and econometrics ;$v3 300 $aDescription based upon print version of record. 311 $a0-470-87251-9 320 $aIncludes bibliographical references and index. 327 $aHandbook of Volatility Models and Their Applications; Contents; Preface; Contributors; 1 Volatility Models; 1.1 Introduction; 1.2 GARCH; 1.2.1 Univariate GARCH; 1.2.1.1 Structure of GARCH Models; 1.2.1.2 Early GARCH Models; 1.2.1.3 Probability Distributions for zt; 1.2.1.4 New GARCH Models; 1.2.1.5 Explanation of Volatility Clustering; 1.2.1.6 Literature and Software; 1.2.1.7 Applications of Univariate GARCH; 1.2.2 Multivariate GARCH; 1.2.2.1 Structure of MGARCH Models; 1.2.2.2 Conditional Correlations; 1.2.2.3 Factor Models; 1.3 Stochastic Volatility; 1.3.1 Leverage Effect; 1.3.2 Estimation 327 $a1.3.3 Multivariate SV Models1.3.4 Model Selection; 1.3.5 Empirical Example: S&P 500; 1.3.6 Literature; 1.4 Realized Volatility; 1.4.1 Realized Variance; 1.4.1.1 Empirical Application; 1.4.2 Realized Covariance; 1.4.2.1 Realized Quadratic Covariation; 1.4.2.2 Realized Bipower Covariation; Acknowledgments; part one Autoregressive Conditional Heteroskedasticity and Stochastic Volatility; 2 Nonlinear Models for Autoregressive Conditional Heteroskedasticity; 2.1 Introduction; 2.2 The Standard GARCH Model; 2.3 Predecessors to Nonlinear GARCH Models; 2.4 Nonlinear ARCH and GARCH Models 327 $a2.4.1 Engle's Nonlinear GARCH Model2.4.2 Nonlinear ARCH Model; 2.4.3 Asymmetric Power GARCH Model; 2.4.4 Smooth Transition GARCH Model; 2.4.5 Double Threshold ARCH Model; 2.4.6 Neural Network ARCH and GARCH Models; 2.4.7 Time-Varying GARCH; 2.4.8 Families of GARCH Models and their Probabilistic Properties; 2.5 Testing Standard GARCH Against Nonlinear GARCH; 2.5.1 Size and Sign Bias Tests; 2.5.2 Testing GARCH Against Smooth Transition GARCH; 2.5.3 Testing GARCH Against Artificial Neural Network GARCH; 2.6 Estimation of Parameters in Nonlinear GARCH Models; 2.6.1 Smooth Transition GARCH 327 $a2.6.2 Neural Network GARCH2.7 Forecasting with Nonlinear GARCH Models; 2.7.1 Smooth Transition GARCH; 2.7.2 Asymmetric Power GARCH; 2.8 Models Based on Multiplicative Decomposition of the Variance; 2.9 Conclusion; Acknowledgments; 3 Mixture and Regime-Switching GARCH Models; 3.1 Introduction; 3.2 Regime-Switching GARCH Models for Asset Returns; 3.2.1 The Regime-Switching Framework; 3.2.2 Modeling the Mixing Weights; 3.2.3 Regime-Switching GARCH Specifications; 3.3 Stationarity and Moment Structure; 3.3.1 Stationarity; 3.3.2 Moment Structure 327 $a3.4 Regime Inference, Likelihood Function, and Volatility Forecasting3.4.1 Determining the Number of Regimes; 3.4.2 Volatility Forecasts; 3.4.3 Application of MS-GARCH Models to Stock Return Indices; 3.5 Application of Mixture GARCH Models to Density Prediction and Value-at-Risk Estimation; 3.5.1 Value-at-Risk; 3.5.2 Data and Models; 3.5.3 Empirical Results; 3.6 Conclusion; Acknowledgments; 4 Forecasting High Dimensional Covariance Matrices; 4.1 Introduction; 4.2 Notation; 4.3 Rolling Window Forecasts; 4.3.1 Sample Covariance; 4.3.2 Observable Factor Covariance 327 $a4.3.3 Statistical Factor Covariance 330 $aA complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions f 410 0$aWiley handbooks in financial engineering and econometrics ;$v3. 606 $aBanks and banking$xEconometric models 606 $aFinance$xEconometric models 606 $aGARCH model 615 0$aBanks and banking$xEconometric models. 615 0$aFinance$xEconometric models. 615 0$aGARCH model. 676 $a332.01/5195 686 $aBUS027000$2bisacsh 700 $aBauwens$b Luc$f1952-$0323257 701 $aHafner$b Christian$0288655 701 $aLaurent$b Sebastien$f1974-$0864502 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141294203321 996 $aHandbook of volatility models and their applications$91929481 997 $aUNINA