[Quantlib-users] Constructing Smile Curve from Quoted Vols in QuantLib

[Goutham M. <gou...@gm...>]

Hi all,

I am pricing FX options and was going through the Quantlib Python Guide to
assist me.
I came to this section on DeltaVolQuotes:

deltaType = ql.DeltaVolQuote.Fwd # Also supports: Spot, PaSpot, PaFwd
atmType = ql.DeltaVolQuote.AtmFwd # Also supports: AtmSpot,
AtmDeltaNeutral, AtmVegaMax, AtmGammaMax,␣
˓→AtmPutCall50
maturity = 1.0
volAtm, vol25DeltaCall, vol25DeltaPut = 0.08, 0.075, 0.095
atmDeltaQuote = ql.DeltaVolQuote(ql.QuoteHandle(ql.SimpleQuote(volAtm)),
deltaType, maturity, atmType)
vol25DeltaPutQuote = ql.DeltaVolQuote(-0.25,
ql.QuoteHandle(ql.SimpleQuote(vol25DeltaPut)), maturity,␣
˓→deltaType)
vol25DeltaCallQuote = ql.DeltaVolQuote(0.25,
ql.QuoteHandle(ql.SimpleQuote(vol25DeltaCall)), maturity,␣
˓→deltaType)

I have the ATMf and 25DRR and 25D Fly quotes for various tenors as attached
in the table below. My doubt is using the code above, is there any way I
can construct the volatility smile curve using QuantLib to derive the
implied volatility of an option.

Vol Table:
ATMF Vol 25D RR 25D Std 10D RR 10 Std
Bid Ask
1m 6.1 6.45 0.25 0.5 0.05 0.15 0.45 0.9 0.16 0.48
2m 6.05 6.4 0.3 0.55 0.1 0.25 0.54 0.99 0.32 0.8
3m 6.05 6.3 0.35 0.6 0.15 0.35 0.63 1.08 0.48 1.12
6m 5.9 6.1 0.45 0.65 0.2 0.35 0.81 1.17 0.64 1.12
9m 5.9 6.1 0.45 0.7 0.2 0.35 0.81 1.26 0.64 1.12
1y 5.9 6.1 0.5 0.7 0.2 0.4 0.9 1.26 0.64 1.28
2y 5.5 6 0.3 0.7 0.25 0.55 0.54 1.26 0.8 1.76
3y 5.2 5.7 0.3 0.7 0.3 0.7 0.54 1.26 0.96 2.24
4y 5.1 5.6 0.1 0.7 0.35 0.85 0.18 1.26 1.12 2.72
5y 5 5.5 -0.1 0.4 0.5 0.9 -0.18 0.72 1.6 2.88

Thank You,
Best,
Goutham