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

[Luigi B. <lui...@gm...>]

Hello,
    this should be fixed by <https://github.com/lballabio/QuantLib/pull/1592>.
It will be in release 1.30.

Luigi

On Tue, Dec 20, 2022 at 11:06 PM Elric StormBringer <
elr...@gm...> wrote:

>
> 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
>>>>>> _______________________________________________
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