VaRDurTest(rugarch)
VaRDurTest()所属R语言包：rugarch
VaR Duration Test
VaR的时间测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Implements the VaR Duration Test of Christoffersen and Pelletier.
克里斯托弗和佩尔蒂埃实现的VaR的时间测试。
用法----------Usage----------
VaRDurTest(alpha, actual, VaR, conf.level = 0.95)
参数----------Arguments----------
参数：alpha
The quantile (coverage) used for the VaR.
分位数（覆盖）的VaR。
参数：actual
A numeric vector of the actual (realized) values.
一个数值向量的实际值（实现）。
参数：VaR
The numeric vector of VaR.
的数字矢量VaR的。
参数：conf.level
The confidence level at which the Null Hypothesis is evaluated.
在零假设被评估的置信水平。
Details
详细信息----------Details----------
The duration of time between VaR violations (no-hits) should ideally be independent and not cluster. Under the null hypothesis of a correctly specified risk model, the no-hit duration should have no memory. Since the only continuous distribution which is memory free is the exponential, the test can conducted on any distribution which embeds the exponential as a restricted case, and a likelihood ratio test then conducted to see whether the restriction holds. Following Christoffersen and Pelletier (2004), the Weibull distribution is used with parameter "b=1" representing the case of the exponential. A future release will include the choice of using a bootstrap method to evaluate the p-value, and until then care should be taken when evaluating series of length less than 1000 as a rule of thumb.
理想情况下，VaR的违规行为（不点击）的持续时间之间的时间应是独立的，不支持聚类。正确的特定风险模型的零假设下，无安打的持续时间应没有记忆。由于这是内存的连续分布的指数，进行测试可以作为一个受限制的情况下，似然比检验，然后进行限制是否持有任何嵌入指数的分布。继克里斯托弗和佩尔蒂埃（2004年），使用参数威布尔分布的b = 1代表情况的指数。未来的版本将包括选择使用自举的方法来评估的p值，在此之前应小心评估系列的长度小于1000时，作为一个经验法则。
值----------Value----------
A list with the following items:<br>
以下项目：参考列表
参数：b
The estimated Weibull parameter which when restricted to the value of 1 results in the Exponential distribution.
估计威布尔参数值限制为1的指数分布的结果。
参数：uLL
The unrestricted Log-Likelihood value.
无限制的对数似然值。
参数：rLL
The restricted Log-Likelihood value.
受限制的对数似然值。
参数：LRp
The Likelihood Ratio Test Statistic.
似然比检验统计量。
参数：H0
The Null Hypothesis.
零假设。
参数：Decision
The on H0 given the confidence level
H0上给定的置信水平
（作者）----------Author(s)----------
Alexios Ghalanos
参考文献----------References----------
duration-based approach, Journal of Financial Econometrics, 2(1), 84–108.
实例----------Examples----------
## Not run: [＃不运行：]
data(dji30ret)
spec = ugarchspec(mean.model = list(armaOrder = c(1,1), include.mean = TRUE),
variance.model = list(model = "gjrGARCH"), distribution.model = "sstd")
fit = ugarchfit(spec, data = dji30ret[1:1000, 1, drop = FALSE])
spec2 = spec
setfixed(spec2)<-as.list(coef(fit))
filt = ugarchfilter(spec2, dji30ret[1001:2500, 1, drop = FALSE], n.old = 1000)
actual = dji30ret[1001:2500,1]
# location+scale invariance allows to use [mu + sigma*q(p,0,1,skew,shape)][位置+标度不变性允许使用[MU +倍标准差* Q（P，0,1，歪斜，形状）]]
VaR = fitted(filt) + sigma(filt)*qdist("sstd", p=0.05, mu = 0, sigma = 1,
skew = coef(fit)["skew"], shape=coef(fit)["shape"])
print(VaRDurTest(0.05, actual, VaR))
# Try with the Normal Distribution (it fails)[尝试与正态分布（失败）]
spec = ugarchspec(mean.model = list(armaOrder = c(1,1), include.mean = TRUE),
variance.model = list(model = "gjrGARCH"), distribution.model = "norm")
fit = ugarchfit(spec, data = dji30ret[1:1000, 1, drop = FALSE])
spec2 = spec
setfixed(spec2)<-as.list(coef(fit))
filt = ugarchfilter(spec2, dji30ret[1001:2500, 1, drop = FALSE], n.old = 1000)
actual = dji30ret[1001:2500,1]
# location+scale invariance allows to use [mu + sigma*q(p,0,1,skew,shape)][位置+标度不变性允许使用[MU +倍标准差* Q（P，0,1，歪斜，形状）]]
VaR = fitted(filt) + sigma(filt)*qdist("norm", p=0.05, mu = 0, sigma = 1)
print(VaRDurTest(0.05, actual, VaR))
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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。
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发表于 2012-9-28 23:41:02
