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2013-07-18_GARCH_example

8 06 00 02 04 acf 04 02 00 acf 06 08 10 series

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Unformatted text preview: q q qq qq qq qq qqq qqq q q q qq qq qq qq −3 −2 −1 0 1 2 Theoretical Quantiles GARCH model for DAX time series 3 ACF plots of residuals and squared residuals (model garch11out) 0.8 0.6 0.0 0.2 0.4 ACF 0.4 0.2 0.0 ACF 0.6 0.8 1.0 Series garch11out\$residuals^2 1.0 Series garch11out\$residuals 0 5 10 15 20 25 30 Lag 0 5 10 15 20 25 30 Lag GARCH model for DAX time series Assessing normality of residuals (model garch11out) > shapiro.test(garch11out\$residuals) Shapiro-Wilk normality test data: garch11out\$residuals W = 0.9851, p-value = 5.446e-13 GARCH model for DAX time series res <- na.omit(garch11out\$residuals) hist(garch11out\$residuals, col="blue", br=100, freq=F) curve(dnorm(x, mean=mean(res), sd=sd(res)), add=TRUE, col="black", lwd=3) curve(dt(x-mean(res), df=5), add=TRUE, col="red", lwd=3) 0.4 0.2 0.0 Density 0.6 0.8 > > > + > −4 −2 0 2 garch11out\$residuals 4 6 0. 0.0 0.2 Assessing the ﬁt of Normal (black) and t5 (red) distributions to the left tail of model residuals (bars) −4 −2 GARCH model for DAX time series Constructing prediction intervals Normality of residuals wa...
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