Re: [Quantlib-users] Bootstraping difference between python QuantLib and Bloomberg.

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Unfortunately i haven't advance much. Why do i get different results when i bootstrap the curve with the swap and OIS rate helpers?

2018-02-13 7:29 GMT-03:00 Luigi Ballabio <lui...@gm...<mailto:lui...@gm...>>:
Apologies for dropping the ball.  Did you make any progress?

Luigi

On Thu, Jan 25, 2018 at 6:41 PM Jose Pedro Melo <jm...@li...<mailto:jm...@li...>> wrote:
Thanks for the fast response,

I'm not sure which interpolation method Bbg uses, but i tried changing it from cubic to linear in quantlib -PiecewiseLinearZero and PiecewiseCubicZero- to check if that would help, without much success. In the following table i changed both the interpolation method and the start date:

Start Date      End Date        DF      Days    Year Fraction   Interpolation   Output
12/01/2018      12/01/2038      0.386246        7305    20.2916667      Cubic   7.830933
12/01/2018      12/01/2038      0.386246        7305    20.2916667      Linear  7.831006

I changed the settlement days to 0 in order to get the same starting dates. For reference, i tried a basic bootstrapping spreadsheet i had and i'm getting closer result to bloomberg's output.

El 24 ene. 2018 12:54 p. m., Luigi Ballabio <lui...@gm...<mailto:lui...@gm...>> escribió:
To check that, you can try passing the spot date instead of today's date to the curve.  That should cause the start date to go to January 12.

Apart from that, do you know what kind of interpolation Bloomberg uses? That might have an effect too.

On Wed, Jan 24, 2018 at 4:48 PM Jose Pedro Melo <jm...@li...<mailto:jm...@li...>> wrote:
I did the following comparison:

Source  Start Date      End Date        Days    Disc. Factor    Year Fraction   (1/df-1)*360/days       Original Output
Quantlib        10/01/2018      12/01/2038      7307    0.386192        20.2972222      7.831%  7.831
Bloomberg       12/01/2018      12/01/2038      7305    0.382045        20.2916667      7.971%  7.971

There is a small difference in the amount of days used to calculate the rates. I don't know if these difference on start dates could have a relevant impact in the bootsrapping algorithm.

2018-01-24 12:13 GMT-03:00 Luigi Ballabio <lui...@gm...<mailto:lui...@gm...>>:
Did you try using the same formula, or comparing the discount factors directly?  (I'd try to understand if the difference is in the underlying curve or in the equivalentRate calculation.)

On Wed, Jan 24, 2018 at 4:06 PM Jose Pedro Melo <jm...@li...<mailto:jm...@li...>> wrote:
Thanks for the response Luigi,

The 'semiannual' tag appears only to show the swap original frequency. I got the rates using the excel formula =BCurveInt(A1;"zc.mid";B1;"Frequency=Zero"), where A1 is the curve bbg ID and B1 is the date for which you are getting the rate. The formula behind the rates is (1/DF-1)*360/Days.

El 24 ene. 2018 11:45 a. m., "Luigi Ballabio" <lui...@gm...<mailto:lui...@gm...>> escribió:
What is Bloomberg quoting, precisely?  The "Semiannual" they're displaying seems to go against the simple-compounding rate you're extracting...

On Mon, Jan 15, 2018 at 9:28 AM josepedro123 <jm...@li...<mailto:jm...@li...>> wrote:
I'm trying to get the Zero rates implied by the swap CLP curve (ICP). The
following code works but it doesn't produce similar levels compared to
Bloomberg's Zero curve. Here is a snippet of the code:

import QuantLib as ql
import pandas as pd

def create_calendar_chile(start_year,n_years):
    Chile = ql.WeekendsOnly()
    days = [1,14,15,1,21,26,2,16,15,18,19,9,27,1,19,8,17,25,31]
    months = [1,4,4,5,5,6,8,9,9,10,10,11,12,12,12,12]
    name = ['Año Nuevo','Viernes Santo','Sabado Santo','Dia del
Trabajo','Dia de las Glorias Navales','San Pedro y San Pablo','Elecciones
Primarias','Dia de la Virgen del Carmen','Asuncion de la
Virgen','Independencia Nacional','Glorias del Ejercito','Encuentro de dos
mundos','Día de las Iglesias Evangélicas y Protestantes','Día de todos los
Santos','Elecciones Presidenciales y Parlamentarias','Inmaculada
Concepción','Segunda vuelta Presidenciales','Navidad','Feriado Bancario']
    for i in range(n_years+1):
        for x,y in zip(days,months):
            date = ql.Date(x,y,start_year+i)
            Chile.addHoliday(date)
    return Chile
def get_curve(date,swap,currency = 'CLP'):
    calendar = create_calendar_chile(2001,50)
    dayCounter_Act360 = ql.Actual360()
    settlement_days_icp = 2
    # OIS quotes up to 20 years
    ICP = ql.OvernightIndex("ICP", settlement_days_icp, ql.CLPCurrency(),
    calendar, dayCounter_Act360)
    fixingDays = 0

    if currency == 'CLP':
        # setup DepositRateHelper for 0-1 days
        TPM = swap[0]
        months = [3,6,9,12,18]
        swap_month = swap[1:6]
        years =[2,3,4,5,6,7,8,9,10,15,20]
        swap_years = swap[6:]
        helpers =
[ql.DepositRateHelper(ql.QuoteHandle(ql.SimpleQuote(TPM/100)),
                                         ql.Period(1,ql.Days), fixingDays,
                                         calendar, ql.Following, False,
ql.Actual360())]
    else:
        months = [3,6,9,12]
        swap_month = swap[0:3]
        years =[2,3,4,5,6,7,8,9,10,15,20]
        swap_years = swap[4:]
        helpers = []

    # setup OISRateHelper from 3 months to 20 years
    helpers +=
[ql.DepositRateHelper(ql.QuoteHandle(ql.SimpleQuote(rate/100)),
                ql.Period(months,ql.Months),
                settlement_days_icp,
                calendar,
                ql.Following,
                False,
                ql.Actual360())
                for rate, months in zip(swap_month,months)]

    #helpers += [ql.OISRateHelper(settlement_days_icp,
ql.Period(months,ql.Months),
    #
ql.QuoteHandle(ql.SimpleQuote(rate/100)),ICP)
    #                                 for rate, months in
zip(swap_month,months)]

    helpers += [ql.OISRateHelper(settlement_days_icp,
ql.Period(years,ql.Years),

ql.QuoteHandle(ql.SimpleQuote(rate/100)),ICP)
                                     for rate, years in
zip(swap_years,years)]

    icp_curve = ql.PiecewiseCubicZero(date, helpers, ql.Actual360())
    icp_curve.enableExtrapolation()
    return icp_curve
def print_zero(date,yieldcurve):
    day_count = ql.Actual360()
    spots = []
    tenors = []
    for d in yieldcurve.dates():
        yrs = day_count.yearFraction(date, d)
        print(yrs,d)
        compounding = ql.Simple
        freq = ql.Annual
        zero_rate = yieldcurve.zeroRate(yrs, compounding, freq)
        tenors.append(yrs)
        eq_rate =
zero_rate.equivalentRate(day_count,compounding,freq,date,d).rate()
        zero_rate.equivalentRate(day_count,compounding,freq,date,d).rate()
        spots.append(100*eq_rate)

    datatable = {'Years': tenors,'Zero': spots}
    datatable = pd.DataFrame.from_dict(datatable)
    print(datatable)

#Eval. Date
date_ql = ql.Date(10,1,2018)
ql.Settings.instance().evaluationDate = date_ql
swap_clp = [2.5, 2.48, 2.47, 2.48, 2.53, 2.64, 2.82, 3.17, 3.43, 3.62, 3.81,
3.96, 4.09, 4.19, 4.29, 4.45, 4.62]
yieldcurve_clp = get_curve(date_ql,swap_clp)
print_zero(date_ql,yieldcurve_clp)
Results:
     Years      Zero
0    0.000000  0.000000
1    0.002778  2.500000
2    0.255556  2.480773
3    0.508333  2.470668
4    0.763889  2.480488
5    1.025000  2.530187
6    1.522222  2.639855
7    2.036111  2.864088
8    3.050000  3.287675
9    4.063889  3.640960
10   5.077778  3.937202
11   6.091667  4.259577
12   7.111111  4.553990
13   8.122222  4.843389
14   9.136111  5.108135
15  10.150000  5.396163
16  15.225000  6.400718
17  20.297222  7.830561
BBG:
Start            End              Days  DF     Frequency   Zero
10-01-2018  11-01-2018  1   1,000069    ZERO    2,500
12-01-2018  12-04-2018  90  0,993838    ZERO    2,480
12-01-2018  12-07-2018  181 0,987734    ZERO    2,470
12-01-2018  12-10-2018  273 0,981540    ZERO    2,480
12-01-2018  14-01-2019  367 0,974857    ZERO    2,530
12-01-2018  12-07-2019  546 0,961501    ZERO    2,640
12-01-2018  13-01-2020  731 0,944621    SEMIANNUAL  2,887
12-01-2018  12-01-2021  1096    0,908255    SEMIANNUAL  3,318
12-01-2018  12-01-2022  1461    0,870133    SEMIANNUAL  3,678
12-01-2018  12-01-2023  1826    0,832049    SEMIANNUAL  3,980
12-01-2018  12-01-2024  2191    0,792244    SEMIANNUAL  4,309
12-01-2018  13-01-2025  2558    0,753263    SEMIANNUAL  4,610
12-01-2018  12-01-2026  2922    0,715195    SEMIANNUAL  4,906
12-01-2018  12-01-2027  3287    0,679005    SEMIANNUAL  5,178
12-01-2018  12-01-2028  3652    0,642988    SEMIANNUAL  5,473
12-01-2018  12-01-2033  5479    0,502562    SEMIANNUAL  6,504
12-01-2018  12-01-2038  7305    0,382045    SEMIANNUAL  7,971

There is a +10 bp difference in the long term part. I tried using the swap
rate helpers and tweacking the days to get the same year fractions but it
doens't seem to be the problem. I wonder how is the oisratehelper
configurated (payment conventions, rates). Any ideas of where could be my
mistake?
Thanks,

--
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