01073nam a22002771i 450099100043733970753620040907142210.0040920s1990 xxu|||||||||||||||||eng b13220706-39ule_instARCHE-116693ExLSet. EconomiaitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.335.41Marx, Karl32587Karl Marx: Capital /edited by Friedrich Engels. Manifesto of the Communist Party / by Karl Marx and Friedrich EngelsManifesto of the Communist Party2nd ed.Chicago :Encyclopaedia Britannica,c1990XI, 434 p. ;24 cmGreat books of the Western world ;50Engels, Friedrich.b1322070602-04-1423-09-04991000437339707536LE025 SEMS 082 GRE01.5012025000198692le025C. 1-E0.00-l- 00000.i1387703323-09-04Karl Marx694206UNISALENTOle02523-09-04ma -engxxu0105212nam 2200661Ia 450 991014061140332120230725023225.01-282-54774-797866125477440-470-68801-70-470-68802-5(CKB)2670000000014746(EBL)514415(OCoLC)609862847(SSID)ssj0000356704(PQKBManifestationID)11275000(PQKBTitleCode)TC0000356704(PQKBWorkID)10350294(PQKB)11490533(MiAaPQ)EBC514415(Au-PeEL)EBL514415(CaPaEBR)ebr10377794(CaONFJC)MIL254774(EXLCZ)99267000000001474620091217d2010 uy 0engur|n|---|||||txtccrARCH models for financial applications[electronic resource] /Evdokia Xekalaki, Stavros DegiannakisChichester ;Hoboken John Wiley & Sons20101 online resource (560 p.)Description based upon print version of record.0-470-06630-X Includes bibliographical references and index.ARCH 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 algorithms2.2.3 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 tests2.7.5 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 example3.3 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 innovations5.2 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 models7.3 Intraday realized volatility andARFIMAXmodels in G@RCH 4.2 OxMetrics: an empirical exampleAutoregressive 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 tFinanceMathematical modelsAutoregression (Statistics)FinanceMathematical models.Autoregression (Statistics)332.015195332.01519536Xekalaki Evdokia614604Degiannakis Stavros614605MiAaPQMiAaPQMiAaPQBOOK9910140611403321ARCH models for financial applications1131618UNINA