LEADER 05212nam 2200661Ia 450 001 9910140611403321 005 20230725023225.0 010 $a1-282-54774-7 010 $a9786612547744 010 $a0-470-68801-7 010 $a0-470-68802-5 035 $a(CKB)2670000000014746 035 $a(EBL)514415 035 $a(OCoLC)609862847 035 $a(SSID)ssj0000356704 035 $a(PQKBManifestationID)11275000 035 $a(PQKBTitleCode)TC0000356704 035 $a(PQKBWorkID)10350294 035 $a(PQKB)11490533 035 $a(MiAaPQ)EBC514415 035 $a(Au-PeEL)EBL514415 035 $a(CaPaEBR)ebr10377794 035 $a(CaONFJC)MIL254774 035 $a(EXLCZ)992670000000014746 100 $a20091217d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aARCH models for financial applications$b[electronic resource] /$fEvdokia Xekalaki, Stavros Degiannakis 210 $aChichester ;$aHoboken $cJohn Wiley & Sons$d2010 215 $a1 online resource (560 p.) 300 $aDescription based upon print version of record. 311 $a0-470-06630-X 320 $aIncludes bibliographical references and index. 327 $aARCH Models for Financial Applications; Contents; Preface; Notation; 1 What is an ARCH process?; 1.1 Introduction; 1.2 The autoregressive conditionally heteroscedastic process; 1.3 The leverage effect; 1.4 The non-trading period effect; 1.5 The non-synchronous trading effect; 1.6 The relationship between conditional variance and conditional mean; 1.6.1 The ARCH in mean model; 1.6.2 Volatility and serial correlation; 2 ARCH volatility specifications; 2.1 Model specifications; 2.2 Methods of estimation; 2.2.1 Maximum likelihood estimation; 2.2.2 Numerical estimation algorithms 327 $a2.2.3 Quasi-maximum likelihood estimation2.2.4 Other estimation methods; 2.3 Estimating the GARCH model with EViews 6: an empirical example; 2.4 Asymmetric conditional volatility specifications; 2.5 Simulating ARCH models using EViews; 2.6 Estimating asymmetric ARCH models with G@RCH 4.2 OxMetrics: an empirical example; 2.7 Misspecification tests; 2.7.1 The Box-Pierce and Ljung-Box Q statistics; 2.7.2 Tse's residual based diagnostic test for conditional heteroscedasticity; 2.7.3 Engle's Lagrange multiplier test; 2.7.4 Engle and Ng's sign bias tests 327 $a2.7.5 The Breusch-Pagan, Godfrey, Glejser, Harvey and White tests2.7.6 The Wald, likelihood ratio and Lagrange multiplier tests; 2.8 Other ARCH volatility specifications; 2.8.1 Regime-switching ARCH models; 2.8.2 Extended ARCH models; 2.9 Other methods of volatility modelling; 2.10 Interpretation of the ARCH process; Appendix; 3 Fractionally integrated ARCH models; 3.1 Fractionally integrated ARCH model specifications; 3.2 Estimating fractionally integrated ARCH models using G@RCH 4.2 OxMetrics: an empirical example 327 $a3.3 A more detailed investigation of the normality of the standardized residuals: goodness-of-fit tests3.3.1 EDF tests; 3.3.2 Chi-square tests; 3.3.3 QQ plots; 3.3.4 Goodness-of-fit tests using EViews and G@RCH; Appendix; 4 Volatility forecasting: an empirical example using EViews 6; 4.1 One-step-ahead volatility forecasting; 4.2 Ten-step-ahead volatility forecasting; Appendix; 5 Other distributional assumptions; 5.1 Non-normally distributed standardized innovations 327 $a5.2 Estimating ARCH models with non-normally distributed standardized innovations using G@RCH 4.2 OxMetrics: an empirical example5.3 Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: an empirical example; 5.4 Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: the logl object; Appendix; 6 Volatility forecasting: an empirical example using G@RCH Ox; Appendix; 7 Intraday realized volatility models; 7.1 Realized volatility; 7.2 Intraday volatility models 327 $a7.3 Intraday realized volatility andARFIMAXmodels in G@RCH 4.2 OxMetrics: an empirical example 330 $aAutoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features:Presents a comprehensive overview of both t 606 $aFinance$xMathematical models 606 $aAutoregression (Statistics) 615 0$aFinance$xMathematical models. 615 0$aAutoregression (Statistics) 676 $a332.015195 676 $a332.01519536 700 $aXekalaki$b Evdokia$0614604 701 $aDegiannakis$b Stavros$0614605 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140611403321 996 $aARCH models for financial applications$91131618 997 $aUNINA