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* GARCH Model Fit *
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Conditional Variance Dynamics
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GARCH Model : realGARCH(1,1)
Mean Model : ARFIMA(0,0,0)
Distribution : norm
Optimal Parameters
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Estimate Std. Error t value Pr(>|t|)
omega -1.00000 NA NA NA
alpha1 0.05000 NA NA NA
beta1 0.70000 NA NA NA
eta11 0.10000 NA NA NA
eta21 0.05000 NA NA NA
delta 1.00000 NA NA NA
lambda 0.54203 NA NA NA
xi -9.41859 NA NA NA
Robust Standard Errors:
Estimate Std. Error t value Pr(>|t|)
omega -1.00000 NA NA NA
alpha1 0.05000 NA NA NA
beta1 0.70000 NA NA NA
eta11 0.10000 NA NA NA
eta21 0.05000 NA NA NA
delta 1.00000 NA NA NA
lambda 0.54203 NA NA NA
xi -9.41859 NA NA NA
failed to invert hessian
LogLikelihood : -1.1
Information Criteria
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Weighted Ljung-Box Test on Standardized Residuals
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statistic p-value
Lag[1] 1.797 1.800e-01
Lag[2*(p+q)+(p+q)-1][2] 7.018 1.179e-02
Lag[4*(p+q)+(p+q)-1][5] 29.001 6.043e-08
d.o.f=0
H0 : No serial correlation
Weighted Ljung-Box Test on Standardized Squared Residuals
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statistic p-value
Lag[1] 974.8 0
Lag[2*(p+q)+(p+q)-1][5] 2314.8 0
Lag[4*(p+q)+(p+q)-1][9] 3787.7 0
d.o.f=2
Weighted ARCH LM Tests
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Statistic Shape Scale P-Value
ARCH Lag[3] NaN 0.500 2.000 NaN
ARCH Lag[5] NaN 1.440 1.667 NaN
ARCH Lag[7] NaN 2.315 1.543 NaN
Error in t.default(grad) : 参数不是矩阵