| summary-methods {fGarch} | R Documentation |
Summary methods for GARCH Modelling.
Generic function
Summary function for objects of class "fGARCH".
The first five sections return the title, the call, the mean and variance formula, the conditional distribution and the type of standard errors:
Title:
GARCH Modelling
Call:
garchFit(~ garch(1, 1), data = garchSim(), trace = FALSE)
Mean and Variance Equation:
~arch(0)
Conditional Distribution:
norm
Std. Errors:
based on Hessian
The next three sections return the estimated coefficients, and
an error analysis including standard errors, t values, and
probabilities, as well as the log Likelihood values from
optimization:
Coefficient(s):
mu omega alpha1 beta1
-5.79788e-05 7.93017e-06 1.59456e-01 2.30772e-01
Error Analysis:
Estimate Std. Error t value Pr(>|t|)
mu -5.798e-05 2.582e-04 -0.225 0.822
omega 7.930e-06 5.309e-06 1.494 0.135
alpha1 1.595e-01 1.026e-01 1.554 0.120
beta1 2.308e-01 4.203e-01 0.549 0.583
Log Likelihood:
-843.3991 normalized: -Inf
The next section provides results on standardized residuals
tests, including statistic and p values, and on information
criterion statistic including AIC, BIC, SIC, and HQIC:
Standardized Residuals Tests:
Statistic p-Value
Jarque-Bera Test R Chi^2 0.4172129 0.8117146
Shapiro-Wilk Test R W 0.9957817 0.8566985
Ljung-Box Test R Q(10) 13.05581 0.2205680
Ljung-Box Test R Q(15) 14.40879 0.4947788
Ljung-Box Test R Q(20) 38.15456 0.008478302
Ljung-Box Test R^2 Q(10) 7.619134 0.6659837
Ljung-Box Test R^2 Q(15) 13.89721 0.5333388
Ljung-Box Test R^2 Q(20) 15.61716 0.7400728
LM Arch Test R TR^2 7.049963 0.8542942
Information Criterion Statistics:
AIC BIC SIC HQIC
8.473991 8.539957 8.473212 8.500687
Diethelm Wuertz for the Rmetrics R-port.
## garchSim - x = garchSim(n = 200) ## garchFit - fit = garchFit(formula = x ~ garch(1, 1), data = x, trace = FALSE) summary(fit)