The basic GARCH(1,1) model is given by

"Bill which does not besure that the variant Take Conditionally Heteroscedastic (ARC photoably depend tions? of west ang Models Conditionally Heteroscedastic (ARC - Our example of a full amodel would be where of - outme , lose Condi eteroscedastic (ARC . This is an ARCH() model. . Instead of calling the variance of. in the other Way of Writing ARCH Models . For illustration, coredder an ARCH(1). Inared of the . The two are different ways of expriming routly esting for 'ARCH Effects' 1 the residuals of an estimated model can following step run sity postulated Boost rearendon string the residuals, de. The for EACH of anbe s in run the regresam sting for 'ARCH Effects' 11 Obtain f from this respondon The test statitle Is defined on The the writerof correlation) from the last regresshome and b alerted em The mall and allerzathe hypotheses now value from the y' distributor. then reject the bull Note that the ARCH bed & ple practi go wepp died diner le ARCH effect in S&:P500 daily returns roblemns with ARCH (o) Models around some of these probasis is a GARCit model alised ARCH (GARCH) Models I Bollersdey (1956) developed the GARCH model which of - est end.. + al- period (on? ) want the fitted variance from the model THE CARCHILD model which Is eralised ARCH (GARCH) Models 1I CC therefore a for amore widely cheralised ARCH (GARCH) Models III of - outand , tou + mad at float wings caeralised ARCH (GARCH) Models IV . An infinite number of successive substitutions would ytek Heralised ARCH (GARCH) Models V "GARCHO thei GARCE(1 1) model to a ility clustering in the data . Why is GARCH better then ARCH? Unconditional Variance under the GARCH . The useandalocal vetinure of to is given by when on + 3