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肖恩 · 2024年08月06日

题目中如何判断是type I还是type II Error?

NO.PZ2018122701000033

问题如下:

Basel II requires a backtest of a bank’s internal value at risk (VaR) model (IMA). Assume the bank’s ten-day 99% VaR is $1 million (minimum of 99% is hard-wired per Basel). The null hypothesis is: the VaR model is accurate. Out of 1,000 observations, 25 exceptions are observed (we saw the actual loss exceed the VaR 25 out of 1000 observations).  (Binomial CDF)

选项:

A.

We will probably call the VaR model good (accurate) but we risk a Type I error.

B.

We will probably call the VaR model good (accurate) but we risk a Type II error.

C.

We will probably call the model bad (inaccurate) but we risk a Type I error.

D.

We will probably call the model bad (inaccurate) but we risk a Type II error.

解释:

C is correct.

考点 : Backtesting VaR

解析 :H0 : the VaR model is accurate. Hα: the VaR model is inaccurate.

Z=xpTp(1p)T=251%×10001%×(11%)×1000=4.77Z=\frac{x-pT}{\sqrt{p(1-p)T}}=\frac{25-1\%\times1000}{\sqrt{1\%\times(1-1\%)\times1000}}=4.77

As 4.77 is larger than 2.58, we reject the null hypothesis. Therefore, the model is bad model, and this implies a risk of type I error.

如果是bad model就存在 type I error,good model就存在type II error是这样吗?

2 个答案

品职答疑小助手雍 · 2024年08月09日

是因为我们只能根据exception的数量发现它超出了常规var能接受的数量,所以已经判断它在这个样本下是一个bad model了。

在基于对它是bad model的判断下,我们只可能犯第一类错误了。(即这模型可能原本是好的,只不过这个样本刚好不给力)

品职答疑小助手雍 · 2024年08月07日

同学你好,model实际上是好的,但是检测却错误得拒绝了它,这是type 1 error。

model实际是不准确的,但是检测却错误得接受了它,这是type 2 error。

肖恩 · 2024年08月07日

概念我是清楚的,就是在这道题目里面,是不是因为有exceptions,所以只能知道有没有type I error,但没有办法检测是否有type II error,所以我们只能选C?

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NO.PZ2018122701000033 问题如下 Basel II requires a backtest of a bank’s internal value risk (VaR) mol (IMA). Assume the bank’s ten-y 99% Vis $1 million (minimum of 99% is harwireper Basel). The null hypothesis is: the Vmol is accurate. Out of 1,000 observations, 25 exceptions are observed (we sthe actuloss exceethe V25 out of 1000 observations).  (BinomiC) We will probably call the Vmol good (accurate) but we risk a Type I error. We will probably call the Vmol good (accurate) but we risk a Type II error. We will probably call the mol bad (inaccurate) but we risk a Type I error. We will probably call the mol bad (inaccurate) but we risk a Type II error. C is correct. 考点 Backtesting V解析 H0 : the Vmol is accurate. Hα: the Vmol is inaccurate.Z=x−pTp(1−p)T=25−1%×10001%×(1−1%)×1000=4.77Z=\frac{x-pT}{\sqrt{p(1-p)T}}=\frac{25-1\%\times1000}{\sqrt{1\%\times(1-1\%)\times1000}}=4.77Z=p(1−p)T​x−pT​=1%×(1−1%)×1000​25−1%×1000​=4.774.77 is larger th2.58, we rejethe null hypothesis. Therefore, the mol is bmol, anthis implies a risk of type I error. 大概能懂题意,我们对银行的一个模型进行backtesing, 然后银行的模型给的VaR是99%,而我们实际测出来的是1000次25个exception。所以我们的结论是这个模型不准,但是因为原假设H0是模型是准确的,而我们的结果拒绝了原假设,所以我们犯了一类错误?有点懵了,那我们既然犯错误了,那对于这个mol不准确的结论成立吗?或者说这个犯一类错误的意义是啥,有点没懂。不知道表达清楚没有。

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