2013-07-18_GARCH_example

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Unformatted text preview: 0.2 0.4 ACF 0.4 0.2 0.0 0.0 ACF 0.6 0.8 0.8 1.0 Series diff(log(DAX))^2 1.0 Series diff(log(DAX)) 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.00 0.02 0.04 0.06 0.08 0.10 0.12 Lag Lag GARCH model for DAX time series Fitting GARCH(1,1) model... > garch11 <- garch(diff(log(DAX)), order = c(1, 1)) > summary(garch11) Call: garch(x = diff(log(DAX)), order = c(1, 1)) Model: GARCH(1,1) Residuals: Min 1Q -12.18398 -0.47968 Median 0.04949 3Q 0.65746 Max 4.48048 Coefficient(s): Estimate Std. Error t value Pr(>|t|) a0 4.639e-06 7.560e-07 6.137 8.42e-10 *** a1 6.833e-02 1.125e-02 6.073 1.25e-09 *** b1 8.891e-01 1.652e-02 53.817 < 2e-16 *** --Signif. codes: 0 ‘***‘ 0.001 ‘**‘ 0.01 ‘*‘ 0.05 ‘.‘ 0.1 ‘ ‘ 1 GARCH model for DAX time series ...ﬁtting GARCH(1,1) model Diagnostic Tests: Jarque Bera Test data: Residuals X-squared = 12946.6, df = 2, p-value < 2.2e-16 Box-Ljung test data: Squared.Residuals X-squared = 0.1357, df = 1, p-value = 0.7126 GARCH model for DAX time series All GARCH model outputs > ls(garch11...
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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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