> I have one made by a chinese people, it is very clear to show the calculate
> steps of percentile.inc, and percentile.exc. If you do not mind the Chinese
> characters, I will share it with you.
>
> regards
>
> Alex
>
>
> 2014-08-12 21:22 GMT+08:00 Peter Caspers <
[hidden email]>:
>
>> there doesn't seem to be any detailled documentation in excel's online
>> help on that function, but
>> in wikipedia the method is explained (I didn't verify) under
>> "Microsoft Excel's Algorithm"
>>
>>
http://en.wikipedia.org/wiki/Percentile>>
>> which is clearly different from what is done in ql.
>>
>> Peter
>>
>>
>> On 12 August 2014 14:17, Yuanhao Zhang <
[hidden email]> wrote:
>> > Many thanks, I thought QuantLib method is more close to the function of
>> > excel 2010 percentile.inc, right?
>> >
>> > Alex
>> >
>> >
>> > 2014-08-09 18:02 GMT+08:00 Peter Caspers <
[hidden email]>:
>> >
>> >> 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
>> >> >>>> >> >
>> >> >>>> >> >
>> >> >>>> >> >
>> >> >>>> >> >
>> >> >>>> >> >
>> >> >>>> >> > ------------------------------------------------------------------------------
>> >> >>>> >> > Infragistics Professional
>> >> >>>> >> > Build stunning WinForms apps today!
>> >> >>>> >> > Reboot your WinForms applications with our WinForms controls.
>> >> >>>> >> > Build a bridge from your legacy apps to the future.
>> >> >>>> >> >
>> >> >>>> >> >
>> >> >>>> >> >
>> >> >>>> >> >
>> >> >>>> >> >
http://pubads.g.doubleclick.net/gampad/clk?id=153845071&iu=/4140/ostg.clktrk>> >> >>>> >> > _______________________________________________
>> >> >>>> >> > QuantLib-users mailing list
>> >> >>>> >> >
[hidden email]
>> >> >>>> >> >
https://lists.sourceforge.net/lists/listinfo/quantlib-users>> >> >>>> >> >
>> >> >>>> >>
>> >> >>>> >>
>> >> >>>> >>
>> >> >>>> >> --
>> >> >>>> >> <
https://implementingquantlib.blogspot.com>
>> >> >>>> >> <
https://twitter.com/lballabio>
>> >> >>>> >
>> >> >>>> >
>> >> >>>>
>> >> >>>>
>> >> >>>>
>> >> >>>> --
>> >> >>>> <
https://implementingquantlib.blogspot.com>
>> >> >>>> <
https://twitter.com/lballabio>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
>> >> >>>
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>> >> >>> QuantLib-users mailing list
>> >> >>>
[hidden email]
>> >> >>>
https://lists.sourceforge.net/lists/listinfo/quantlib-users>> >> >>>
>> >> >
>> >> >
>> >> >
>> >> > --
>> >> > <
https://implementingquantlib.blogspot.com>
>> >> > <
https://twitter.com/lballabio>
>> >
>> >
>
>
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