QuantLib-Python: Splitting the NPV of an option into intrinsic & time

Question

I am right now working throught the "cookbook" of Goutham Balaraman & Luigi Ballabio. By the way a very nice introduction into QuantLib-Python and a good starting point :-)

In section four there is the following example that prices a simple option with Black&Scholes:

from QuantLib import *

today = Date(7, March, 2014)
Settings.instance().evaluationDate = today

# The Instrument
option = EuropeanOption( PlainVanillaPayoff(Option.Call, 100.0),
                         EuropeanExercise(Date(7, June, 2014)))

# The Market
u = SimpleQuote(100.0)      # set todays value of the underlying
r = SimpleQuote(0.01)       # set risk-free rate 
sigma = SimpleQuote(0.20)   # set volatility
riskFreeCurve = FlatForward(0, TARGET(), QuoteHandle(r), Actual360())
volatility = BlackConstantVol(0, TARGET(), QuoteHandle(sigma), Actual360())

# The Model
process = BlackScholesProcess( QuoteHandle(u), 
                               YieldTermStructureHandle(riskFreeCurve),
                               BlackVolTermStructureHandle(volatility))

# The Pricing Engine
engine = AnalyticEuropeanEngine(process)

# The Result
option.setPricingEngine(engine)
print( "NPV: ", option.NPV() )

The code spits out the NPV of the option.

In university I once learned that the value of an option could be split up into an 'intrinsic' and 'time' part. Is it possible to achive this with QouantLib-Python?

 NPV_intrinsic = max([ 0 , u.value() - option.getStrike() ]) 

The above does not work because 'option.getStrike()' doesn't exist :-(

Thank you very much!

Answer 1

The method you're looking for is option.payoff(), which returns the payoff P of the option as a function object; the intrinsic value would then be P(u.value()).

However, the method is available in C++ but not yet exported to Python. I suggest you open an issue about this at https://github.com/lballabio/QuantLib-SWIG/issues so that the developers can pick it up.