Re: [Quantlib-users] QuantLib Arguments for (BarrierOption) ImpliedVolatility -> 'targetValue' and'process'

[Elric S. <elr...@gm...>]

I actually used the code below for a test-case (Up-and-Out Barrier Option).
I know the arguments to specify the trade is correct, because I can get
'PV', and 'greeks'. But when I tried to get the 'Implied Volatility', it
fails with the following error :  return _QuantLib.
BarrierOption_impliedVolatility(self, targetValue, process, accuracy,
maxEvaluations, minVol, maxVol)

RuntimeError: root not bracketed: f[0.0001,4] -> [-nan(ind),1.127331e+01]



Below is the python code : ...... Not sure if it is due to 'arguments'
being insufficient, or the solver cannot solve for the Implied-vol, or
something else.


# Import required library
> from QuantLib import *


# Barrier Option: Up-and-Out Call
# Strike 100, Barrier 150, Rebate 50, Exercise date 4 years

#Set up the global evaluation date to today
today = Date(28,February,2020)
Settings.instance().evaluationDate = today

# Specify option
# ql.BarrierOption(barrierType, barrier, rebate, payoff, exercise)
# the option below sets Up-and-Out barrier, with Barrier Level of 150,
European Vanilla Call as the underlying payoff
# of European Call Strike and European Expiry date in exercise
option = BarrierOption(Barrier.UpOut, 150.0, 50.0,
                       PlainVanillaPayoff(Option.Call, 100.0),
                       EuropeanExercise(Date(29, February, 2024)))

# We will now pass the market data: spot price : 100, risk-free rate:
1% and sigma: 30%
# Underlying Price
u = SimpleQuote(100)
# Risk-free Rate
r = SimpleQuote(0.01)
# Sigma
sigma = SimpleQuote(0.30)

# Build flat curves and volatility
riskFreeCurve = FlatForward(0, TARGET(), QuoteHandle(r), Actual360())
volatility = BlackConstantVol(0, TARGET(), QuoteHandle(sigma), Actual360())

# Build the pricing engine by encapsulating the market data in a
Black-Scholes process
# Stochastic Process
process = BlackScholesProcess(QuoteHandle(u),
                              YieldTermStructureHandle(riskFreeCurve),
                              BlackVolTermStructureHandle(volatility))

# Build the engine (based on an analytic formula) and set it to the
option for evaluation
option.setPricingEngine(AnalyticBarrierEngine(process))

# Change the market data to get new option pricing.
# Set initial value and define h
u0 = u.value(); h=0.01
P0 = option.NPV()
print('the initial option price is', np.round(P0, 4))

# Bump up the price by h
u.setValue(u0+h)
P_plus = option.NPV()
print('the bumped up option price is', np.round(P_plus, 4))

# Bump down the price by h
u.setValue(u0-h)
P_minus = option.NPV()
print('the bumped down option price is', np.round(P_minus, 4), '\n')

# Set the price back to its current value
u.setValue(u0)

# Calculate Greeks: Delta, Gamma, Vega, Theta, Rho
delta = (P_plus - P_minus)/(2*h)
gamma = (P_plus - 2*P0 + P_minus)/(h*h)

# Update quote for rho calculation
r0 = r.value(); h1 = 0.0001
r.setValue(r0+h); P_plus = option.NPV()
r.setValue(r0)

# Rho
rho = (P_plus - P0)/h1

# Update quote for sigma calculation
sigma0 = sigma.value() ; h = 0.0001
sigma.setValue(sigma0+h) ; P_plus = option.NPV()
sigma.setValue(sigma0)

# Vega
vega = (P_plus - P0)/h

# Update quote to calculate theta
Settings.instance().evaluationDate = today+1
P1 = option.NPV()
h = 1.0/365

# Theta
theta = (P1-P0)/h

print(f'OptionPrice: {P0: .2f}, Delta: {delta: .2f}, Gamma: {gamma:
.4f}, Theta: {theta: .2f}, \
    Vega: {vega: .2f}, Rho: {rho: .2f}')

option.impliedVolatility(22.06, process)


---------




On Fri, 2 Dec 2022 at 21:01, Ashish Bansal <ash...@gm...> wrote:

> Specific to IV, i didn't calculate IV for barriers so not sure. The IV is
> not implied for all the option types like for averaging options, it doesn't
> work. However, i do see the same being implemented for Barriers:
>
> https://github.com/lballabio/QuantLib/blob/2536f0e3db681f4cb8f4972f09d561e8f085b5ea/ql/instruments/barrieroption.cpp#L51
> Volatility BarrierOption::impliedVolatility(
> Real targetValue,
> const ext::shared_ptr<GeneralizedBlackScholesProcess>& process,
> Real accuracy,
> Size maxEvaluations,
> Volatility minVol,
> Volatility maxVol)
>
> What error are you getting? Try to enter more arguments in it and try.
>
> Regards
> Ashish
>
> On Fri, 2 Dec 2022 at 17:15, Elric StormBringer <
> elr...@gm...> wrote:
>
>> thanks everyone, very grateful for all the helpful responses.
>>
>> perhaps to be a bit more clear; I was having problems with the FX Touch
>> Barrier Options
>> 1. When I specified the option is Vanilla European : option =
>> ql.VanillaOption(payoff, exercise)
>> -> using the option.impliedVolatility(premium, process) *** works ***
>> [see screenshot below[/
>>
>> 2. But when I specify the product type to be anything else, it no longer
>> works, and I just get error messages...
>> -> so, is it we need more special arguments for non-European-Vanilla? Or
>> does the Implied Volatility work *** ONLY *** for European Vanilla Options?
>>
>> [image: image.png]
>>
>>
>>
>>
>> [image: image.png]
>>
>>
>> On Fri, 2 Dec 2022 at 17:41, Ashish Bansal <ash...@gm...>
>> wrote:
>>
>>> Hi Elric,
>>>
>>> If you want a basic code for barrier then I wrote 1 in this thread:
>>> https://sourceforge.net/p/quantlib/mailman/message/37670218/
>>>
>>> can also refer to the examples of barrier options in this test suite if
>>> you are comfortable with C++:
>>>
>>> https://github.com/lballabio/QuantLib/blob/master/test-suite/barrieroption.cpp
>>>
>>> Thanks
>>> Ashish
>>>
>>> On Wed, 30 Nov 2022 at 18:21, Jonathan Sweemer <sw...@gm...>
>>> wrote:
>>>
>>>> Hi Kiann,
>>>>
>>>> I'm surprised that the error you're seeing isn't related to the
>>>> arguments you pass to BlackScholesProcess. You should be passing term
>>>> structure objects instead of numerical values.
>>>>
>>>> See these links for more information:
>>>>
>>>> 1.
>>>> https://quantlib-python-docs.readthedocs.io/en/latest/stochastic_processes.html#blackscholesprocess
>>>> 2.
>>>> https://github.com/lballabio/QuantLib-SWIG/blob/master/SWIG/stochasticprocess.i#L116-L121
>>>> 3.
>>>> https://stackoverflow.com/questions/4891490/calculating-europeanoptionimpliedvolatility-in-quantlib-python
>>>>
>>>>
>>>>
>>>> On Wed, Nov 30, 2022 at 8:13 PM Elric StormBringer <
>>>> elr...@gm...> wrote:
>>>>
>>>>> Hi there, a noob using/investigationg QuantLib library via python.
>>>>>
>>>>> Great job there guys!
>>>>>
>>>>> I've been browsing the online documentation, but still having problem
>>>>> trying to find the 'arguments' and 'fields' inside each.
>>>>> For example, after I've defined the trade details/market-data/engine
>>>>> for : ql.BarrierOption.impliedVolatility
>>>>> -> I am trying to use .ImpliedVolatility.
>>>>> -> However, I am getting errors from my input fields
>>>>> : option_.impliedVolatility(0.01, ql.BlackScholesProcess(1.0, 0.0, 0.3))
>>>>>
>>>>> Can I check, what are the syntax of the data-fields to be used? The
>>>>> error message is : impliedVolatility() missing 2 required positional
>>>>> arguments: 'targetValue' and 'process'
>>>>>
>>>>> Kind regards
>>>>> Kiann
>>>>> _______________________________________________
>>>>> QuantLib-users mailing list
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>>>>>
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