ugarchroll-methods(rugarch)
ugarchroll-methods()所属R语言包：rugarch
function: Univariate GARCH Rolling Density Forecast and Backtesting
功能：单变量GARCH车辆密度预测及回溯测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Method for creating rolling density forecast from ARMA-GARCH models with option for refitting every n periods and some multicore parallel functionality.
创建滚动密度ARMA-GARCH模型的预测，从选择重新安装每N个周期，一些多核并行功能的方法。
用法----------Usage----------
ugarchroll(spec, data, n.ahead = 1, forecast.length = 500, refit.every = 25,
refit.window = c("recursive", "moving"), parallel = FALSE,
parallel.control = list(pkg = c("multicore", "snowfall"), cores = 2), solver = "solnp",
fit.control = list(), solver.control = list(), calculate.VaR = TRUE,
VaR.alpha = c(0.01, 0.05), ...)
参数----------Arguments----------
参数：spec
A univariate GARCH spec object specifiying the desired model for testing.
单变量GARCH规范的对象specifiying所需的模型进行测试。
参数：data
A univariate dataset.
单变量的数据集。
参数：n.ahead
The number of periods to forecast.
期预测。
参数：forecast.length
The length of the total forecast for which out of sample data from the dataset will be excluded for testing.
满分从数据集的样本数据的总预测的长度将被排除用于测试。
参数：refit.every
Determines every how many periods the model is re-estimated.
确定每个模型重新估计多少时间。
参数：refit.window
Whether the refit is done on an expanding window including all the previous data or a moving window, the length of the window determined by the argument above (“refit.every”).
是否完成的改装包括所有以前的数据或一移动窗口，由上述的参数（“refit.every”）确定的窗口的长度扩大窗口。
参数：parallel
Whether to make use of parallel processing on multicore systems.
是否利用多核系统上的并行处理。
参数：parallel.control
The parallel control options including the type of package for performing the parallel calculations ("multicore" for non-windows O/S and "snowfall" for all O/S), and the number of cores to make use of.
并行控制选项，包括包的类型进行并行计算（多核非Windows O / S和“降雪”，所有的O / S），核心数量的利用。
参数：solver
The solver to use.
求解器使用。
参数：fit.control
Control parameters parameters passed to the fitting function.
控制参数通过拟合函数。
参数：solver.control
Control parameters passed to the solver.
控制参数传递的解算器。
参数：calculate.VaR
Whether to calculate forecast Value at Risk during the estimation.
无论是计算预测值在评估风险。
参数：VaR.alpha
The Value at Risk tail level to calculate.
在风险尾水平的价值来计算。
参数：...
.
。
Details
详细信息----------Details----------
GARCH models generate a partially time varying density based on the variation in the conditional sigma and mean values (skewness and shape are usually not time varying in GARCH models unless the underlying distribution has an interaction with the conditional sigma). The function first generates rolling forecasts of the ARMA-GARCH model and then rescales the density from a standardized (0, 1, skew, shape) to the one representing the underlying return process (mu, sigma, skew, shape). Given this information it is then a simple matter to generate any measure of risk through the analytical evaluation of some type of function of the time varying density. The function calculates one such measure (VaR), but since the full time varying density parameters are returned, the user can calculate many others (see for example partial moments based measures or the Pedersen-Satchell family of measures).<br> The argument refit.every determines every how many periods the fit is recalculated and the total forecast length actually calculated. For example, for a forecast length of 500 and refit.every of 25, this is 20 windows of 25 periods each for a total actual forecast length of 500. However, for a refit.every of 30, we take the floor of the division of 500 by 30 which is 16 windows of 30 periods each for a total actual forecast length of 480 (16 x 30). The important thing to remember about the refit.every is that it acts like the n.roll argument in the ugarchforecast function as it determines the number of rolls to perform. For example for n.ahead of 1 and refit.every of 25, the forecast is rolled every day using the filtered (actual) data of the previous period while for n.ahead of 1 and refit.every of 1 we will get 1 n.ahead forecasts for every day after which the model is refitted and reforecast for a total of 500 refits (when length.forecast is 500)!<br> The function has 2 main methods for viewing the data, a standard plot method and a new report methods (see class uGARCHroll for details on how to use these methods).
GARCH模型生成的局部在有条件的sigma和平均值（偏斜度和形状通常是不GARCH模型中的随时间变化的，除非底层分布具有的相互作用与该条件西格玛）的基础上的变化的随时间变化的密度。该函数首先生成的滚动预测，ARMA-GARCH模型，然后重新调整密度从一个标准化的（0，1，倾斜，形状）的潜在回报（MU，SIGMA，倾斜，形状）。给定该信息，然后，它是一个简单的事情产生的风险的任何措施，通过随时间变化的密度的函数的一些类型的分析评价。该函数计算一个这样的措施（VAR），但因为完整的随时间变化的密度参数返回时，用户可以计算出其他许多人（见例如部分基于矩措施的彼得森，的萨切尔家庭的措施）。<BR>的参数refit.every确定每个多少时间重新计算拟合实际计算和预测总长度。例如，对于长度为500的预测和refit.every25，这是25周期，每个周期的总的实际预测长度为500的20个窗口。然而，对于一个refit.every30 500 30 30期间是16个窗口，每一个的总实际的预测长度为480（16×30）的划分，我们把地板。重要的是要记住的refit.every的是，它的作用就像ugarchforecast功能的n.roll参数，因为它决定了轧辊执行。对于例如，和refit.every 25为n.ahead，预测推出每天使用过滤（实际）数据的前一段时间，而我们将得到n.ahead 1和refit.every的1 1 N提前预测的每一天之后，重新安装和重新预测模型的共500整修（当length.forecast为500）！<BR>该函数有2种主要的方法来查看数据，标准样地法和新的报告方法（见类uGARCHroll如何使用这些方法的详细信息）。
值----------Value----------
An object of class uGARCHroll.
对象的类uGARCHroll。
（作者）----------Author(s)----------
Alexios Ghalanos
参见----------See Also----------
For specification ugarchspec, fitting ugarchfit, filtering ugarchfilter, forecasting ugarchforecast, simulation ugarchsim, parameter distribution and uncertainty ugarchdistribution, bootstrap forecast ugarchboot.
对于规范ugarchspec，配件ugarchfit，过滤ugarchfilter，预测ugarchforecast，模拟ugarchsim，参数分布和不确定性ugarchdistribution，引导预测 X>。
实例----------Examples----------
## Not run: [＃不运行：]
data(sp500ret)
ctrl = list(rho = 1, delta = 1e-9, outer.iter = 100, tol = 1e-7)
spec = ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1,1)),
mean.model = list(armaOrder = c(0,0), include.mean = TRUE),
distribution.model = "std")
sp500.bktest = ugarchroll(spec, data = sp500ret, n.ahead = 1,
forecast.length = 100, refit.every = 25, refit.window = "recursive",
solver = "solnp", fit.control = list(), solver.control = ctrl,
calculate.VaR = TRUE, VaR.alpha = c(0.01, 0.025, 0.05))
report(sp500.bktest, type="VaR", n.ahead = 1, VaR.alpha = 0.01,
conf.level = 0.95)
report(sp500.bktest, type="fpm")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-28 23:40:21
