2013-07-18_GARCH_example

00 004 008 lag 012 00 02 04 06 08 10 series

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Unformatted text preview: of standardized residuals q −3 −2 −1 0 1 2 Theoretical Quantiles GARCH model for DAX time series 3 q ACF plots of residuals and squared residuals 0.00 0.04 0.08 Lag 0.12 0.0 0.2 0.4 0.6 0.8 1.0 Series garch11$residuals^2 ACF 0.0 0.2 0.4 0.6 0.8 1.0 ACF Series garch11$residuals 0.00 0.04 0.08 Lag GARCH model for DAX time series 0.12 Assessing normality of GARCH(1,1) residuals > shapiro.test(garch11$residuals) Shapiro-Wilk normality test data: garch11$residuals W = 0.9478, p-value < 2.2e-16 GARCH model for DAX time series Creating dlDAXout variable without outlier > dlDAX <- diff(log(DAX)) > dlDAXout <- dlDAX[-which.min(dlDAX)] > length(dlDAX)-1 == length(dlDAXout) [1] TRUE GARCH model for DAX time series Fitting GARCH(1,1) model on data without outlier... > garch11out <- garch(dlDAXout, order = c(1, 1)) > summary(garch11out) Call: garch(x = dlDAXout, order = c(1, 1)) Model: GARCH(1,1) Residuals: Min 1Q -4.27247 -0.49145 Median 0.05182 3Q 0.68511 Max 6...
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This note was uploaded on 08/04/2013 for the course ECON 201 taught by Professor Vandewaal during the Spring '09 term at Waterloo.

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