05795nam 2200769Ia 450 991014129420332120200520144314.01-280-59147-197866136213061-118-27205-61-118-27203-X1-118-27199-8(CKB)2670000000167332(EBL)836590(SSID)ssj0000636247(PQKBManifestationID)11420825(PQKBTitleCode)TC0000636247(PQKBWorkID)10660370(PQKB)11610114(Au-PeEL)EBL836590(CaPaEBR)ebr10546576(CaONFJC)MIL362130(PPN)170610829(FR-PaCSA)88813016(MiAaPQ)EBC836590(OCoLC)785426815(EXLCZ)99267000000016733220111130d2012 uy 0engur|n|---|||||txtccrHandbook of volatility models and their applications /Luc Bauwens, Christian Hafner, Sebastien Laurent1st ed.Hoboken, N.J. John Wiley & Sons, Inc.20121 online resource (565 p.)Wiley handbooks in financial engineering and econometrics ;3Description based upon print version of record.0-470-87251-9 Includes bibliographical references and index.Handbook 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 Estimation1.3.3 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 Models2.4.1 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 GARCH2.6.2 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 Structure3.4 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 Covariance4.3.3 Statistical Factor CovarianceA 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 fWiley handbooks in financial engineering and econometrics ;3.Banks and bankingEconometric modelsFinanceEconometric modelsGARCH modelBanks and bankingEconometric models.FinanceEconometric models.GARCH model.332.01/5195BUS027000bisacshBauwens Luc1952-323257Hafner Christian288655Laurent Sebastien1974-864502MiAaPQMiAaPQMiAaPQBOOK9910141294203321Handbook of volatility models and their applications1929481UNINA