在quantlib-python中计算EuropeanOptionImpliedVolatility

Question

I have R code that uses RQuantlib library. In order to run it from python I am using RPy2. I know python has its own bindings for quantlib (quantlib-python). I'd like to switch from R to python completely.

Please let me know how I can run the following using quantlib-python

import rpy2.robjects as robjects

robjects.r('library(RQuantLib)')
x = robjects.r('x<-EuropeanOptionImpliedVolatility(type="call", value=11.10, underlying=100,strike=100, dividendYield=0.01, riskFreeRate=0.03,maturity=0.5, volatility=0.4)')
print x

Sample run:

$ python vol.py 
Loading required package: Rcpp
Implied Volatility for EuropeanOptionImpliedVolatility is 0.381

Answer 1

You'll need a bit of setup. For convenience, and unless you get name clashes, you better import everything:

from QuantLib import *

then, create the option, which needs an exercise and a payoff:

exercise = EuropeanExercise(Date(3,August,2011))
payoff = PlainVanillaPayoff(Option.Call, 100.0)
option = EuropeanOption(payoff,exercise)

(note that you'll need an exercise date, not a time to maturity.)

Now, whether you want to price it or get its implied volatility, you'll have to setup a Black-Scholes process. There's a bit of machinery involved, since you can't just pass a value, say, of the risk-free rate: you'll need a full curve, so you'll create a flat one and wrap it in a handle. Ditto for dividend yield and vol; the underlying value goes in a quote. (I'm not explaining what all the objects are; comment if you need it.)

S = QuoteHandle(SimpleQuote(100.0))
r = YieldTermStructureHandle(FlatForward(0, TARGET(), 0.03, Actual360()))
q = YieldTermStructureHandle(FlatForward(0, TARGET(), 0.01, Actual360()))
sigma = BlackVolTermStructureHandle(BlackConstantVol(0, TARGET(), 0.20, Actual360()))
process = BlackScholesMertonProcess(S,q,r,sigma)

(the volatility won't actually be used for implied-vol calculation, but you need one anyway.)

Now, for implied volatility you'll call:

option.impliedVolatility(11.10, process)

and for pricing:

engine = AnalyticEuropeanEngine(process)
option.setPricingEngine(engine)
option.NPV()

You might use other features (wrap rates in a quote so you can change them later, etc.) but this should get you started.