我尝试着回答您的问题：
现有中石化10块（假设），那么未来可能长可能跌，假如限定时间为一年，那么最大损失是0，不是不可能发生，只是概率几乎为零。盈利则也可能无限大。这里不考虑盈利，因为VaR以及CVaR都是对损失风险的考虑。
那么按照VaR的基本原理，在5%的置信水平下价格下跌到8块。那么这个8就是得到的VaR。在满足股价GARCH模型的情况下，应该为均值-置信系数*波动率，当然其他的定义可能得到不同的结果。
对于CVaR而言，是考虑到价格跌破VaR价格之后的风险水平，结合例子来说就是在已知价格低于8块条件下预测倒置信水平下的概率，基于一般的假设该值等于均值-稍大于置信系数的值*波动率。
看过的东西也忘记差不多了，实在不敢说自己写的这些对。但是60%吧。因为如果是50%的话就是均匀分布，形同无知。

My friend at floor 2, I have to correct some errors in your explanation:

1) when you have 中石化10块, the extreme loss is 10块 when 中石化 stock price is 0. 按照VaR的基本原理，在5%的置信水平下价格下跌到8块, then VaR is 2. In real life, VaR can tell you that under normal condition (keep in mind the market condition is normal), there is 5% chance that you can loss more than $2 or 95% chance that your loss will not loss more than $2.

2) One drawback of VaR is that it does not tell you how much you can loss for 5% chance. It just tell your loss is more than $2. As you know, the extreme loss is $10, you probably want to know what is the distribution of loss between $2 and $10. That's thing CVaR try to do.

3) GARCCH model is used to forecast the volatility of stock price return (波动率). When you use historical method to calculate the VaR, the volatility implied from history may not reflect the future's. GARCH is better way in forecasting future's volatility.



Hope it will help!

could you guys give an intuitive explanation why VaR is not subaddictive?

The intuitive reason is, I think,  that VaR is a quantile estimator and it ignores the extreme points beyond the significant level. (The standard deviation as a measure does not ignore extreme points hence it is subadditive).

The common example:
Bond A, default probability is 4%, with a loss of 100%, otherwise return is 0. The 5% VAR for this bond is actually 0, because 96% probability, return will be zero. The loss (extreme points) is ignored.
Bond B, same default likelihood. and 5% VAR is also 0%. A portfolio of 100 A and 100B will have a VAR(5%) higher than 0. You can do the caculation yourself. The reason is that those ignored extreme points, when combined, are significant.

3# FRMNY2008
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