garch(tseries)
garch()所属R语言包：tseries
Fit GARCH Models to Time Series
适合时间序列的GARCH模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fit a Generalized Autoregressive Conditional Heteroscedastic GARCH(p, q) time series model to the data by computing the maximum-likelihood estimates of the conditionally normal model.
有条件的正常模式计算的最大似然估计的数据拟合的广义自回归条件异方差GARCH（P，Q）的时间序列模型。
用法----------Usage----------
garch(x, order = c(1, 1), series = NULL, control = garch.control(...), ...)
garch.control(maxiter = 200, trace = TRUE, start = NULL,
grad = c("analytical","numerical"), abstol = max(1e-20, .Machine$double.eps^2),
reltol = max(1e-10, .Machine$double.eps^(2/3)), xtol = sqrt(.Machine$double.eps),
falsetol = 1e2 * .Machine$double.eps, ...)
参数----------Arguments----------
参数：x
a numeric vector or time series.
一个数值向量或时间系列。
参数：order
a two dimensional integer vector giving the orders of the model to fit. order[2] corresponds to the ARCH part and order[1] to the GARCH part.
发出订单的模式，以适应一个二维整数向量。 order[2]对应的ARCH部分和order[1]GARCH部分。
参数：series
name for the series. Defaults to deparse(substitute(x)).
名称为系列。默认为deparse(substitute(x))的。
参数：control
a list of control parameters as set up by garch.control.
列表中的控制参数，成立了由garch.control。
参数：maxiter
gives the maximum number of log-likelihood function evaluations maxiter and the maximum number of iterations 2*maxiter the optimizer is allowed to compute.
给出了最大数量的对数似然函数评价maxiter和最大迭代次数2*maxiter的优化计算。
参数：trace
logical. Trace optimizer output?
逻辑。跟踪优化输出？
参数：start
If given this numeric vector is used as the initial estimate of the GARCH coefficients. Default initialization is to set the GARCH parameters to slightly positive values and to initialize the intercept such that the unconditional variance of the initial GARCH is equal to the variance of x.
如果这个数字矢量作为初步估计的GARCH系数。缺省的初始化是设置的GARCH参数到稍微的正值和初始化使得初始的GARCH无条件方差是等于x的方差的截距。
参数：grad
character indicating whether analytical gradients or a numerical approximation is used for the optimization.
字符指示是否分析梯度或用于优化的数值近似。
参数：abstol
absolute function convergence tolerance.
绝对的功能收敛性。
参数：reltol
relative function convergence tolerance.
相对功能收敛性。
参数：xtol
coefficient-convergence tolerance.
系数收敛性。
参数：falsetol
false convergence tolerance.
虚假收敛公差。
参数：...
additional arguments for qr when computing the asymptotic covariance matrix.
额外的参数为qr时计算的渐近协方差矩阵。
Details
详细信息----------Details----------
garch uses a Quasi-Newton optimizer to find the maximum likelihood estimates of the conditionally normal model. The first max(p, q) values are assumed to be fixed. The optimizer uses a hessian approximation computed from the BFGS update. Only a Cholesky factor of the Hessian approximation is stored. For more details see Dennis et al. (1981), Dennis and Mei (1979), Dennis and More (1977), and Goldfarb (1976). The gradient is either computed analytically or using a numerical approximation.
garch采用了拟牛顿优化找到的最大似然估计有条件的正常模式。第一最大值（P，Q）的值被假设是固定的。优化器使用一个麻的近似计算的BFGS更新的。只有Cholesky因子的Hessian近似的存储位置。有关详细信息，请参阅丹尼斯等。 （1981年），丹尼斯和梅（1979），丹尼斯和（1977），和戈德法布（1976年）。梯度计算分析，或使用一个数值逼近。
值----------Value----------
A list of class "garch" with the following elements:
类"garch"包含下列元素的列表：
参数：order
the order of the fitted model.
拟合模型的顺序。
参数：coef
estimated GARCH coefficients for the fitted model.
估计GARCH系数拟合模型。
参数：n.likeli
the negative log-likelihood function evaluated at the coefficient estimates (apart from some constant).
评估的负对数似然函数的系数估计值（除了一些常数）。
参数：n.used
the number of observations of x.
的数量观察x。
参数：residuals
the series of residuals.
残差的系列。
参数：fitted.values
the bivariate series of conditional standard deviation predictions for x.
二元一系列的条件标准差预测x。
参数：series
the name of the series x.
名称的一系列x。
参数：frequency
the frequency of the series x.
频率的一系列x。
参数：call
the call of the garch function.
的呼唤garch功能。
参数：vcov
outer product of gradient estimate of the asymptotic-theory covariance matrix for the coefficient estimates.
梯度渐近理论的协方差矩阵的估计系数估计的外积。
（作者）----------Author(s)----------
A. Trapletti, the whole GARCH part;
D. M. Gay, the FORTRAN optimizer
参考文献----------References----------
ARCH Models: Properties, Estimation and Testing. J. Economic Surveys 7 305–362.
Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics 31, 307–327.
Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica 50, 987–1008.
Algorithm 573 — An Adaptive Nonlinear Least-Squares Algorithm. ACM Transactions on Mathematical Software 7, 369–383.
Two New Unconstrained Optimization Algorithms which use Function and Gradient Values. J. Optim. Theory Applic. 28, 453–482.
Quasi-Newton Methods, Motivation and Theory. SIAM Rev. 19, 46–89.
Factorized Variable Metric Methods for Unconstrained Optimization. Math. Comput. 30, 796–811.
参见----------See Also----------
summary.garch for summarizing GARCH model fits; garch-methods for further methods.
summary.garch总结GARCH模型的适合，“garch-methods进一步的方法。
实例----------Examples----------
n <- 1100
a <- c(0.1, 0.5, 0.2) # ARCH(2) coefficients[ARCH（2）系数]
e <- rnorm(n)
x <- double(n)
x[1:2] <- rnorm(2, sd = sqrt(a[1]/(1.0-a[2]-a[3])))
for(i in 3:n) # Generate ARCH(2) process[生成ARCH（2）工艺]
{
x[i] <- e[i]*sqrt(a[1]+a[2]*x[i-1]^2+a[3]*x[i-2]^2)
}
x <- ts(x[101:1100])
x.arch <- garch(x, order = c(0,2)) # Fit ARCH(2) [适合ARCH（2）]
summary(x.arch) # Diagnostic tests[诊断测试]
plot(x.arch)
data(EuStockMarkets)
dax <- diff(log(EuStockMarkets))[,"DAX"]
dax.garch <- garch(dax) # Fit a GARCH(1,1) to DAX returns[适合DAX返回一个GARCH（1,1）]
summary(dax.garch) # ARCH effects are filtered. However, [ARCH效应过滤。但是，]
plot(dax.garch) # conditional normality seems to be violated[有条件的常态似乎被侵犯]
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注：
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发表于 2012-10-1 12:38:15
