interesting. yes, at first sight the one in ql seems to be of type 1.
maybe we should think about adding type 5 to attract some hydrologists
?
Peter
On 8 August 2014 11:53, Luigi Ballabio <[hidden email]> wrote:
> Yes, it rather depends on the distribution you assume. In R, there are
> 9 different quantile algorithms:
> <http://stat.ethz.ch/R-manual/R-patched/library/stats/html/quantile.html>.
> I think the one in QuantLib is one of the first three.
>
> Luigi
>
>
> On Fri, Aug 8, 2014 at 10:05 AM, Peter Caspers <[hidden email]> wrote:
>> the "correct" answer depends on how you estimate your distribution
>> from your sample data, doesn't it ? if you use a discrete distribution
>> just assuming equal probability for each observation (like
>> GeneralStatistics, if no weight is given), then the 1% quantile (which
>> by definition = 99% VaR)
>>
>> q = inf ( x | F(x) >= 0.01 )
>>
>> is equal to the third loss (-0.04), because (assuming 250 losses
>> -0.06, -0.05, -0.04, ...) F(-0.06) = 1 / 250 < 0.01, F(-0.05) = 2 /
>> 250 < 0.01, F(-0.04) = 3 / 250 >= 0.01, I think. So QuantLib seems to
>> do a good job here ?
>>
>> Peter
>>
>> On 8 August 2014 02:34, Yuanhao Zhang <[hidden email]> wrote:
>>> Thanks, Luigi. But, I have made the test, the result should be between 0.04,
>>> and 0.05, not exactly the 0.04. But, lower than 0.045. I donot know why.
>>>
>>> regards
>>>
>>> Alex
>>>
>>>
>>> 2014-08-07 22:51 GMT+08:00 Luigi Ballabio <[hidden email]>:
>>>
>>>> The code below doesn't interpolate. In your case, if you ask for 0.99
>>>> VaR, the second loss is 0.05 and the third is 0.04, it just returns
>>>> 0.04.
>>>>
>>>> I don't think I remember the reason for that, though.
>>>>
>>>> Luigi
>>>>
>>>>
>>>> On Thu, Aug 7, 2014 at 1:19 PM, Yuanhao Zhang <[hidden email]>
>>>> wrote:
>>>> > Forgot to attach the excel file there.
>>>> > Hi, I am totally confused by the quantlib percentile function, the code
>>>> > is
>>>> > listed below. I have attached an excel file, you can find the issue in
>>>> > sheet1. If I have 250 sample pnl, the 0.99 percentile is just between
>>>> > the
>>>> > second and third one, use the interpolated function the value I have
>>>> > highlighted there. But, the result of QL is smaller than the highlighted
>>>> > one. And I thought the result of Excel is right, I have read the code,
>>>> > but i
>>>> > can not understand it, could you explain it for me. thanks!
>>>> >
>>>> > Real GeneralStatistics::percentile(Real percent) const {
>>>> >
>>>> > QL_REQUIRE(percent > 0.0 && percent <= 1.0,
>>>> > "percentile (" << percent << ") must be in (0.0,
>>>> > 1.0]");
>>>> >
>>>> > Real sampleWeight = weightSum();
>>>> > QL_REQUIRE(sampleWeight>0.0,
>>>> > "empty sample set");
>>>> >
>>>> > sort();
>>>> >
>>>> > std::vector<std::pair<Real,Real> >::iterator k, l;
>>>> > k = samples_.begin();
>>>> > l = samples_.end()-1;
>>>> > /* the sum of weight is non null, therefore there's
>>>> > at least one sample */
>>>> > Real integral = k->second, target = percent*sampleWeight;
>>>> > while (integral < target && k != l) {
>>>> > k++;
>>>> > integral += k->second;
>>>> > }
>>>> > return k->first;
>>>> > }
>>>> >
>>>> >
>>>> >
>>>> > 2014-08-07 18:10 GMT+08:00 Luigi Ballabio <[hidden email]>:
>>>> >>
>>>> >> What are you getting instead? May you post some sample code that
>>>> >>
>>>> >> reproduces the issue?
>>>> >>
>>>> >> Luigi
>>>> >>
>>>> >> On Wed, Aug 6, 2014 at 6:20 PM, Yuanhao Zhang <[hidden email]>
>>>> >> wrote:
>>>> >> > Does any one used Statistics Value at risk function in QuantLib? I
>>>> >> > try
>>>> >> > to
>>>> >> > use the simple 250 PnL sample to test this function. The confidence
>>>> >> > level is
>>>> >> > 0.99, so the value should be between the second one and the third
>>>> >> > one.
>>>> >> > If
>>>> >> > the second one is -0.04 and the third one is -0.05, so the value is
>>>> >> > -(-0.04+(-0.05))/2=0.045. But, the quantlib value is always smaller
>>>> >> > than
>>>> >> > 0.045. Is that wrong with the calculation logic? I can not figure it
>>>> >> > out
>>>> >> > why
>>>> >> > the value is different. Any one could help me figure it out?
>>>> >> >
>>>> >> > Best regards
>>>> >> >
>>>> >> > Alex
>>>> >> >
>>>> >> >
>>>> >> >
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>>>> >>
>>>> >>
>>>> >>
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>>>> >
>>>> >
>>>>
>>>>
>>>>
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