Handbook of volatility models and their applications / ed. by Luc Bauwens ...

Contributor(s):

Bauwens, Luc, 1952- [Hrsg.]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Wiley handbooks in financial engineering and econometrics ; 3Publisher: Hoboken, NJ : Wiley, 2012Edition: Online-AusgDescription: Online-Ressource (1 online resource (xx, 543 p.)) : illISBN:

9781280591471
1280591471
9781118271995

Subject(s):

Genre/Form:

Aufsatzsammlung

Additional physical formats: 9780470872512 | 9781118271995 | 1280591447 | Druckausg.: Handbook of volatility models and their applications. Hoboken, NJ : Wiley, 2012. XX, 543 S.MSC: MSC: *62-06 | 91-06 | 62P05 | 62M10 | 91B84 | 91G70RVK: RVK: QH 237LOC classification:

HG1601

Online resources:

Zugang im Netz des KIT

Summary: A 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 from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk managementSummary: Intro -- 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 Estimation -- 1.3.3 Multivariate SV Models -- 1.3.4 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 -- 2.4.1 Engle's Nonlinear GARCH Model -- 2.4.2 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.PPN: PPN: 80967095XPackage identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PBE | ZDB-30-PQE

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