How to compute NPV of Latin American swap CLP-TNA (chilean) using quantlib?

Question

I am trying to value the Latin Americans swaps. But CLP-TNA valuation is far off from the actual valuation. Please suggest, what I am missing in below methodology to compute NPV.

# construct discount curve and libor curve
risk_free_rate = 0.01
libor_rate = 0.02
day_count = ql.Actual360()

discount_curve = ql.YieldTermStructureHandle(
     ql.FlatForward(calculation_date, risk_free_rate, day_count)
)

  libor_curve = ql.YieldTermStructureHandle(
  ql.FlatForward(calculation_date, libor_rate, day_count)
)
# CLP index
 CLP_index = ql.OvernightIndex('CLP', 0, ql.CLPCurrency(), ql.WeekendsOnly(), ql.Actual360())

 calendar = ql.WeekendsOnly()
settle_date = calendar.advance(calculation_date, 5, ql.Days)
maturity_date = calendar.advance(settle_date, 10, ql.Years)


  fixed_schedule = ql.Schedule(settle_date, maturity_date, 
                     fixed_leg_tenor, calendar,
                     ql.ModifiedFollowing, ql.ModifiedFollowing,
                     ql.DateGeneration.Forward, False)

  float_schedule = ql.Schedule (settle_date, maturity_date, 
                      float_leg_tenor, calendar,
                      ql.ModifiedFollowing, ql.ModifiedFollowing,
                      ql.DateGeneration.Forward, False)

  notional = 10000000
 fixed_rate = 0.025
fixed_leg_daycount = ql.Actual360()
float_spread = 0.004
 float_leg_daycount = ql.Actual360()

 ir_swap = ql.VanillaSwap(ql.VanillaSwap.Payer, notional, fixed_schedule, 
       fixed_rate, fixed_leg_daycount, float_schedule,
       libor3M_index, float_spread, float_leg_daycount )

 swap_engine = ql.DiscountingSwapEngine(discount_curve)
 ir_swap.setPricingEngine(swap_engine)

Answer 1

Here is an example that might point you in the right direction. As Luigi said the comments, you can't really expect to arrive at comparable values if you are just using flat curves.

So the first step would be to build a curve comparable to Bloomberg. I don't really have any experience with CLP buy looking at the info on BBG it looks like these swap are zero coupon until 18M and pay semi after that.

import QuantLib as ql
today = ql.Date(25,6,2021)
calendar = ql.WeekendsOnly()
ql.Settings.instance().evaluationDate = today
spot = calendar.advance(today, 2, ql.Days)
dayCount = ql.Actual360()

cop_ois_quotes = [
    ('3M', 0.780),
    ('6M', 1.140),
    ('9M', 1.435),
    ('12M', 1.770),
    ('18M', 2.145),
    ('2Y',  2.430),
    ('3Y',  2.745),
    ('4Y',  3.01),
    ('5Y',  3.28),
    ('6Y',  3.53),
    ('7Y',  3.715),
    ('8Y',  3.835),
    ('9Y',  3.93),
    ('10Y', 4.055),
    ('15Y', 4.365),
    ('20Y', 4.465),
]

helpers = []
clp_ois_yts = ql.RelinkableYieldTermStructureHandle()

index = ql.OvernightIndex('CLICP', 0, ql.CLPCurrency(), ql.WeekendsOnly(), dayCount, clp_ois_yts)
for tenor, value in cop_ois_quotes:
    value /= 100
    quote = ql.QuoteHandle(ql.SimpleQuote(value))
    period = ql.Period(tenor)
    paymentFrequency = ql.Semiannual if period.units() > 2 else ql.Once
    helper = ql.OISRateHelper(2, period, quote, index, paymentFrequency=paymentFrequency)
    helpers.append(helper)
clp_ois_crv = ql.PiecewiseLogLinearDiscount(spot, helpers, ql.ActualActual())
clp_ois_crv.enableExtrapolation()
clp_ois_yts.linkTo(clp_ois_crv)

You can then test your curve by pricing some swaps. Here I'm pricing the input instruments so it is expected to yield the same results but you can play around with other dates.

swapType = ql.OvernightIndexedSwap.Payer
nominal = 100

engine = ql.DiscountingSwapEngine(clp_ois_yts)

for tenor, value in cop_ois_quotes:
    value /= 100
    maturity = calendar.advance(spot, ql.Period(tenor))
    freq = '18M' if ql.Period(tenor).units() == 2 else '6M'
    schedule = ql.MakeSchedule(spot, maturity, ql.Period(freq), calendar=calendar)
    ois_swap = ql.OvernightIndexedSwap(swapType, nominal, schedule, 0.0, dayCount, index)
    ois_swap.setPricingEngine(engine)
    print(f"{tenor}: {ois_swap.fairRate()*100:.4f}, {value*100:.4f}")

3M: 0.7800, 0.7800
6M: 1.1400, 1.1400
9M: 1.4350, 1.4350
12M: 1.7700, 1.7700
18M: 2.1450, 2.1450
2Y: 2.4300, 2.4300
3Y: 2.7479, 2.7450
4Y: 3.0100, 3.0100
5Y: 3.2800, 3.2800
6Y: 3.5300, 3.5300
7Y: 3.7150, 3.7150
8Y: 3.8350, 3.8350
9Y: 3.9312, 3.9300
10Y: 4.0550, 4.0550
15Y: 4.3650, 4.3650
20Y: 4.4651, 4.4650