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Re: [Quantlib-users] Help about the asian options
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From: Ashish B. <ash...@gm...> - 2022-06-03 16:27:06
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Will wait for July. May I request to release during early July? if no issues in that? Thanks for all the help and information. Thanks Ashish On Fri, Jun 3, 2022, 2:53 PM Luigi Ballabio <lui...@gm...> wrote: > Version 1.27 will be out sometime in July. The changes included so far > are at <https://github.com/lballabio/QuantLib/milestone/23?closed=1>. > The changes in previous releases are at < > https://github.com/lballabio/QuantLib/releases/>. > > Luigi > > > On Thu, Jun 2, 2022 at 7:23 AM Ashish Bansal <ash...@gm...> > wrote: > >> That is great news. I have been waiting for TW engine for a long time :) >> . Special thanks to Fredrick (@gbfredrik) for adding it. >> >> When is v1.27 planned to be released? Hoping that it will be released on >> SWIG at the same time. Where can I read about other changes in 1.27 so that >> we can plan accordingly? >> >> Thanks >> Ashish >> >> On Thu, 2 Jun 2022 at 01:35, Luigi Ballabio <lui...@gm...> >> wrote: >> >>> Hello, >>> it does mention it now -- the Turnbull-Wakeman engine was added to >>> the repository less than a month ago and will be in release 1.27. It >>> didn't exist in release 1.17. >>> >>> Luigi >>> >>> >>> On Wed, Jun 1, 2022 at 7:55 PM Ashish Bansal <ash...@gm...> >>> wrote: >>> >>>> Luigi, >>>> >>>> You had given me the following link sometime back. It does mention >>>> about the Turnbull Wakeman model based on 2 moment matching: >>>> >>>> https://github.com/lballabio/QuantLib/blob/master/test-suite/asianoptions.cpp >>>> <ql/pricingengines/asian/turnbullwakemanasianengine.hpp> >>>> >>>> [image: image.png] >>>> Even the example is given in the above file. Although the asianoption >>>> is part of test-suite but the Turnbull...engine is part of main QL. Is it >>>> not part of SWIG? i tried in it in python but could not find it. >>>> >>>> How can I use this in python with QL v1.17? >>>> >>>> Regards, >>>> Ashish >>>> >>>> On Mon, 30 May 2022 at 19:39, Ashish Bansal <ash...@gm...> >>>> wrote: >>>> >>>>> Thanks for the responses. I am trying various papers to find the >>>>> moment matching for the asian options. I did find the calculation based on >>>>> Turnbull-Wakeman model here: >>>>> >>>>> https://osf.io/efq6a/download >>>>> >>>>> >>>>> I tried this but did not get good results for options expiring in a >>>>> few months. I will try some other source probably. >>>>> >>>>> >>>>> One last question, does QL's FdBlackScholesAsianengine with DiscreteAveragingAsianOption >>>>> adjust the valuation for trade which are in between the averaging period; >>>>> as mentioned on slide # 9 and 10 of this deck? >>>>> >>>>> >>>>> Regards, >>>>> >>>>> Ashish >>>>> >>>>> On Thu, 26 May 2022 at 13:24, Luigi Ballabio <lui...@gm...> >>>>> wrote: >>>>> >>>>>> I mean that the two engines give the same price, i.e. 6.09. I don't >>>>>> have info on moment matching. >>>>>> >>>>>> On Wed, May 25, 2022 at 6:51 PM Ashish Bansal < >>>>>> ash...@gm...> wrote: >>>>>> >>>>>>> By same price, you mean same as exchange i.e. 4.57. or same as aRA >>>>>>> parameter i.e. 6.09? >>>>>>> >>>>>>> Any guidance on moment matching? I could not find anything online >>>>>>> about it. >>>>>>> >>>>>>> Regards >>>>>>> Ashish >>>>>>> >>>>>>> On Wed, May 25, 2022, 7:02 PM Luigi Ballabio < >>>>>>> lui...@gm...> wrote: >>>>>>> >>>>>>>> Hi, >>>>>>>> no, the engine doesn't do moment matching. I don't think we >>>>>>>> have something ready to use for that in the library. >>>>>>>> As for the accumulator: I confirm it's the sum. If you create a >>>>>>>> virtual Python environment and install the latest QuantLib in it, you can >>>>>>>> try your shortened code with the other constructor for the Asian option >>>>>>>> (the one taking the list of fixings). It returns the same price. >>>>>>>> >>>>>>>> Luigi >>>>>>>> >>>>>>>> >>>>>>>> On Wed, May 25, 2022 at 2:46 PM Ashish Bansal < >>>>>>>> ash...@gm...> wrote: >>>>>>>> >>>>>>>>> Hi Luigi, >>>>>>>>> >>>>>>>>> One question about the QL calculation: Does QL code for the Asian >>>>>>>>> option include *moment matching *of the volatility or do we need >>>>>>>>> to do that before passing to the DiscAvAsOption? >>>>>>>>> >>>>>>>>> Regarding the code, I had attached the full code from our system >>>>>>>>> in my previous email, attaching here too. The shortened version of the >>>>>>>>> code, containing only the QL interaction and excluding excess processing, >>>>>>>>> is also attached here. It includes the trade parameters we are using to >>>>>>>>> evaluate the Asian option. Strangely, the value is coming zero with >>>>>>>>> short-code but with full code in our system, we are getting the values. >>>>>>>>> >>>>>>>>> Following is the NPV that we are getting in our system (using full >>>>>>>>> code) for our trade which has averaging period from 1st March to 31st March >>>>>>>>> 2022 and the market traded premium of 4.56: >>>>>>>>> arithmeticRunningAccumulator NPV >>>>>>>>> 0 4.5711 >>>>>>>>> 1302.88 6.09 >>>>>>>>> >>>>>>>>> Thanks in advance for help. >>>>>>>>> >>>>>>>>> PS: We are using QL1.17 and the vols are sourced from Reuters >>>>>>>>> could be different from implied in the market traded premium. >>>>>>>>> >>>>>>>>> Regards, >>>>>>>>> Ashish >>>>>>>>> >>>>>>>>> On Mon, 23 May 2022 at 22:26, Luigi Ballabio < >>>>>>>>> lui...@gm...> wrote: >>>>>>>>> >>>>>>>>>> May you send sample code and the value of the fixings for one of >>>>>>>>>> those options? Thanks! >>>>>>>>>> >>>>>>>>>> Luigi >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On Mon, May 23, 2022 at 8:32 AM Ashish Bansal < >>>>>>>>>> ash...@gm...> wrote: >>>>>>>>>> >>>>>>>>>>> Luigi, >>>>>>>>>>> >>>>>>>>>>> We ran the asian code, which i sent earlier for a couple of >>>>>>>>>>> asian options traded on exchange. We got prices very close to traded prices >>>>>>>>>>> when we used the value of running accumulator as 1 for march option being >>>>>>>>>>> valued on 17th with past fixing count as 12. But when we used the sum of >>>>>>>>>>> past fixing prices i.e. 1302.88 we got a very different number like 6.09 >>>>>>>>>>> against 4.57 earlier and 14.11 against 12.39 earlier. >>>>>>>>>>> >>>>>>>>>>> Just want to confirm if i am missing something about running >>>>>>>>>>> accumulator and should it be really the sum of all past fixings? >>>>>>>>>>> >>>>>>>>>>> Thanks for help in advance. >>>>>>>>>>> Ashish >>>>>>>>>>> >>>>>>>>>>> On Fri, 20 May 2022 at 14:58, Ashish Bansal < >>>>>>>>>>> ash...@gm...> wrote: >>>>>>>>>>> >>>>>>>>>>>> Ok understood. thanks. >>>>>>>>>>>> >>>>>>>>>>>> On Fri, 20 May 2022 at 14:46, Luigi Ballabio < >>>>>>>>>>>> lui...@gm...> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> It would be the sum of past fixings; for three past fixings of >>>>>>>>>>>>> 100.2, 101.3 and 94.6 it would be 296.1. >>>>>>>>>>>>> >>>>>>>>>>>>> Luigi >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> On Thu, May 19, 2022 at 12:37 PM Ashish Bansal < >>>>>>>>>>>>> ash...@gm...> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>>> Thanks, Luigi for the suggestion. After taking the running >>>>>>>>>>>>>> accumulator, we are able to generate the numbers. What exactly should be >>>>>>>>>>>>>> passed here? The average price of past fixings or just 1 when the trade is >>>>>>>>>>>>>> priced during averaging period? >>>>>>>>>>>>>> >>>>>>>>>>>>>> Thanks for help. >>>>>>>>>>>>>> >>>>>>>>>>>>>> Ashish >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Wed, 18 May 2022 at 21:17, Luigi Ballabio < >>>>>>>>>>>>>> lui...@gm...> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>>> I don't have a 1.7 version to try it, but the check is the >>>>>>>>>>>>>>> one at < >>>>>>>>>>>>>>> https://github.com/lballabio/QuantLib/blob/QuantLib-v1.7/ql/pricingengines/asian/fdblackscholesasianengine.cpp#L51-L53>, >>>>>>>>>>>>>>> suggesting that your running accumulator should be 0 (which makes sense, >>>>>>>>>>>>>>> because you're using arithmetic average, i.e., a sum, and if you don't have >>>>>>>>>>>>>>> any fixings yet the sum should be 0.) >>>>>>>>>>>>>>> Your default seems to be 1 instead. >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Luigi >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> On Wed, May 18, 2022 at 5:17 PM Ashish Bansal < >>>>>>>>>>>>>>> ash...@gm...> wrote: >>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Luigi, >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Our code is not too much but we are using >>>>>>>>>>>>>>>> deprecated functions like FDEuropeanEngine and FDAmericanEngine. >>>>>>>>>>>>>>>> Not sure if using new function called >>>>>>>>>>>>>>>> FdBlackScholesVanillaEngine has any change in the >>>>>>>>>>>>>>>> calculation that our code will have an impact. >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Can we use the DiscreteAveragingAsianOption without the >>>>>>>>>>>>>>>> overloaded parameter of pastfixings under 1.7? >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> Ashish >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>> On Wed, 18 May 2022 at 20:00, Luigi Ballabio < >>>>>>>>>>>>>>>> lui...@gm...> wrote: >>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Hi — it could be. Any particular reason you're still >>>>>>>>>>>>>>>>> using a version from 4 years ago? If you have a substantial amount of code >>>>>>>>>>>>>>>>> that depends on QuantLib, it might require some work to update because some >>>>>>>>>>>>>>>>> methods were deprecated and then removed in the meantime; and of course >>>>>>>>>>>>>>>>> you'll probably have some validation process in place when updating, so >>>>>>>>>>>>>>>>> that's work too. But on the other hand, you're missing 4 years worth of >>>>>>>>>>>>>>>>> bug fixings... >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> Luigi >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> On Wed, May 18, 2022 at 4:19 PM Ashish Bansal < >>>>>>>>>>>>>>>>> ash...@gm...> wrote: >>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> We tried again and got the same error. >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> Could it be due to the old version (1.7) we are using? I >>>>>>>>>>>>>>>>>> see in the release notes of Apr-2021 that the overload for past fixing was >>>>>>>>>>>>>>>>>> added: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> - Added an overloaded constructor for Asian options >>>>>>>>>>>>>>>>>> that takes all past fixings and thus allows to reprice them correctly when >>>>>>>>>>>>>>>>>> the evaluation date changes (thanks to Jack Gillett). >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> Ashish >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> On Wed, 18 May 2022 at 19:14, Jack G < >>>>>>>>>>>>>>>>>> jac...@gm...> wrote: >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> Hi Ashish, >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> I have a better answer for you with a screenshot >>>>>>>>>>>>>>>>>>> awaiting moderation as it's quite large, but in the meantime, I was able to >>>>>>>>>>>>>>>>>>> run your code without problems using the python at the bottom of this email >>>>>>>>>>>>>>>>>>> (although I didn't check the numbers). >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> If there is something you're doing specifically to >>>>>>>>>>>>>>>>>>> generate the error, could you please attach a minimal reproduction of it? >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> Best, >>>>>>>>>>>>>>>>>>> Jack >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> import QuantLib as ql >>>>>>>>>>>>>>>>>>> from copy import deepcopy >>>>>>>>>>>>>>>>>>> from datetime import datetime >>>>>>>>>>>>>>>>>>> from datetime import timedelta >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> # DEFAULT_PERIODS = [ql.Period(f"{i}d") for i in >>>>>>>>>>>>>>>>>>> range(0,31)] >>>>>>>>>>>>>>>>>>> DEFAULT_PERIODS = [ql.Period("6M"), ql.Period("12M"), >>>>>>>>>>>>>>>>>>> ql.Period("18M"), ql.Period("24M")] >>>>>>>>>>>>>>>>>>> DEFAULT_tGRID = 100 >>>>>>>>>>>>>>>>>>> DEFAULT_xGRID = 100 >>>>>>>>>>>>>>>>>>> DEFAULT_aGRID = 50 >>>>>>>>>>>>>>>>>>> DEFAULT_aRA = 1 # arithmeticRunningAccumulator >>>>>>>>>>>>>>>>>>> DEFAULT_PASTFIXINGS = 1 >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> class AsianOptionPrice: >>>>>>>>>>>>>>>>>>> def __init__(self, **x): >>>>>>>>>>>>>>>>>>> for k, v in x.items(): >>>>>>>>>>>>>>>>>>> setattr(self, k, v) >>>>>>>>>>>>>>>>>>> self.tGrid = x.get("tGrid", DEFAULT_tGRID) >>>>>>>>>>>>>>>>>>> self.xGrid = x.get("xGrid", DEFAULT_xGRID) >>>>>>>>>>>>>>>>>>> self.aGrid = x.get("aGrid", DEFAULT_aGRID) >>>>>>>>>>>>>>>>>>> self.periods = x.get("periods", DEFAULT_PERIODS) >>>>>>>>>>>>>>>>>>> self.pastFixings = x.get("pastFixings", >>>>>>>>>>>>>>>>>>> DEFAULT_PASTFIXINGS) >>>>>>>>>>>>>>>>>>> self.aRA = x.get("arithmeticRunningAccumulator", >>>>>>>>>>>>>>>>>>> DEFAULT_aRA) >>>>>>>>>>>>>>>>>>> self.keyList = [k for k, v in x.items()] >>>>>>>>>>>>>>>>>>> self.dist = 10**(-2) >>>>>>>>>>>>>>>>>>> self.date_format = "%d-%m-%Y" >>>>>>>>>>>>>>>>>>> self.value_date = ql.DateParser.parseFormatted( >>>>>>>>>>>>>>>>>>> self.valueDate, '%d-%m-%Y') >>>>>>>>>>>>>>>>>>> self.expiry_date = ql.DateParser.parseFormatted( >>>>>>>>>>>>>>>>>>> self.expiryDate, '%d-%m-%Y') >>>>>>>>>>>>>>>>>>> ql.Settings.instance().evaluationDate = >>>>>>>>>>>>>>>>>>> self.value_date >>>>>>>>>>>>>>>>>>> self.exDay = >>>>>>>>>>>>>>>>>>> ql.DateParser.parseFormatted(self.expiryDate, '%d-%m-%Y') >>>>>>>>>>>>>>>>>>> # Volatility input is required for running the >>>>>>>>>>>>>>>>>>> computing implied volatility function. This volatility is NOT used in the >>>>>>>>>>>>>>>>>>> implied volatility calculation. >>>>>>>>>>>>>>>>>>> self.vol_for_iv_calc = 0.10 >>>>>>>>>>>>>>>>>>> self.IV = None >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> def >>>>>>>>>>>>>>>>>>> generate_fixture_dates(self,start_date,end_date,period): >>>>>>>>>>>>>>>>>>> schedule = ql.MakeSchedule(start_date, end_date, >>>>>>>>>>>>>>>>>>> period) >>>>>>>>>>>>>>>>>>> fixture_dates = [_date for _date in schedule if >>>>>>>>>>>>>>>>>>> _date.weekday() not in [1,7]] >>>>>>>>>>>>>>>>>>> return fixture_dates >>>>>>>>>>>>>>>>>>> def asian(self): >>>>>>>>>>>>>>>>>>> """ >>>>>>>>>>>>>>>>>>> arithmeticRunningAccumulator(aRA) >>>>>>>>>>>>>>>>>>> """ >>>>>>>>>>>>>>>>>>> arithmeticRunningAccumulator = self.aRA >>>>>>>>>>>>>>>>>>> pastFixings = self.pastFixings >>>>>>>>>>>>>>>>>>> print(pastFixings) >>>>>>>>>>>>>>>>>>> option_type = self.optionType.lower() >>>>>>>>>>>>>>>>>>> if option_type not in ["call", "put"]: >>>>>>>>>>>>>>>>>>> raise Exception( >>>>>>>>>>>>>>>>>>> f"Option Type is Neither Call nor Put, >>>>>>>>>>>>>>>>>>> Option type is {option_type}") >>>>>>>>>>>>>>>>>>> option_type = ql.Option.Call if option_type == >>>>>>>>>>>>>>>>>>> "call" else ql.Option.Put >>>>>>>>>>>>>>>>>>> strike = self.strikePrice >>>>>>>>>>>>>>>>>>> today = self.value_date >>>>>>>>>>>>>>>>>>> print(today) >>>>>>>>>>>>>>>>>>> fixingStartDate >>>>>>>>>>>>>>>>>>> =ql.DateParser.parseFormatted(self.fixingStartDate, '%d-%m-%Y') >>>>>>>>>>>>>>>>>>> print("#"*40,fixingStartDate) >>>>>>>>>>>>>>>>>>> periods = self.periods >>>>>>>>>>>>>>>>>>> asianFutureFixingDates = >>>>>>>>>>>>>>>>>>> self.generate_fixture_dates(fixingStartDate,self.expiry_date,ql.Period("1d")) >>>>>>>>>>>>>>>>>>> print("#"*40,asianFutureFixingDates) >>>>>>>>>>>>>>>>>>> asianExpiryDate = self.expiry_date >>>>>>>>>>>>>>>>>>> vanillaPayoff = >>>>>>>>>>>>>>>>>>> ql.PlainVanillaPayoff(option_type, strike) >>>>>>>>>>>>>>>>>>> europeanExercise = >>>>>>>>>>>>>>>>>>> ql.EuropeanExercise(asianExpiryDate) >>>>>>>>>>>>>>>>>>> arithmeticAverage = ql.Average().Arithmetic >>>>>>>>>>>>>>>>>>> discreteArithmeticAsianOption = >>>>>>>>>>>>>>>>>>> ql.DiscreteAveragingAsianOption( >>>>>>>>>>>>>>>>>>> arithmeticAverage, >>>>>>>>>>>>>>>>>>> arithmeticRunningAccumulator, pastFixings, asianFutureFixingDates, >>>>>>>>>>>>>>>>>>> vanillaPayoff, europeanExercise) >>>>>>>>>>>>>>>>>>> return discreteArithmeticAsianOption >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> def create_arg_list(self, Option=None, >>>>>>>>>>>>>>>>>>> compute_implied_vol=None, implied_vol_am_put=None): >>>>>>>>>>>>>>>>>>> ''' >>>>>>>>>>>>>>>>>>> The arguments with disturbances added. Will >>>>>>>>>>>>>>>>>>> be used for calculation of Greeks >>>>>>>>>>>>>>>>>>> using numerical differentiation. >>>>>>>>>>>>>>>>>>> Creating a dictionary of arguments that will >>>>>>>>>>>>>>>>>>> be used for option price and greeks calculation. >>>>>>>>>>>>>>>>>>> ''' >>>>>>>>>>>>>>>>>>> self.riskFreeRate = self.riskFreeRate/100 >>>>>>>>>>>>>>>>>>> originalDict_ = {k: getattr(self, k) for k in >>>>>>>>>>>>>>>>>>> self.keyList} >>>>>>>>>>>>>>>>>>> ListDict = [] >>>>>>>>>>>>>>>>>>> for i in range(4): >>>>>>>>>>>>>>>>>>> ListDict.append(deepcopy(originalDict_)) >>>>>>>>>>>>>>>>>>> ListDict[1]['riskFreeRate'] = self.riskFreeRate >>>>>>>>>>>>>>>>>>> + self.dist >>>>>>>>>>>>>>>>>>> ListDict[2]['volatility'] = self.volatility + >>>>>>>>>>>>>>>>>>> self.dist >>>>>>>>>>>>>>>>>>> ListDict[3]['expiryDate'] = (datetime.strptime( >>>>>>>>>>>>>>>>>>> self.expiryDate, self.date_format) - >>>>>>>>>>>>>>>>>>> timedelta(days=1)).strftime(self.date_format) >>>>>>>>>>>>>>>>>>> print(ListDict) >>>>>>>>>>>>>>>>>>> return(ListDict) >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> def bs_process(self, create_process_for_iv=None, >>>>>>>>>>>>>>>>>>> use_IV=None): >>>>>>>>>>>>>>>>>>> """ >>>>>>>>>>>>>>>>>>> Creating the Black-Scholes process for option >>>>>>>>>>>>>>>>>>> valuation. >>>>>>>>>>>>>>>>>>> """ >>>>>>>>>>>>>>>>>>> riskFreeTS = ql.YieldTermStructureHandle( >>>>>>>>>>>>>>>>>>> ql.FlatForward(self.value_date, >>>>>>>>>>>>>>>>>>> self.riskFreeRate, ql.Actual365Fixed())) >>>>>>>>>>>>>>>>>>> dividendTS = ql.YieldTermStructureHandle( >>>>>>>>>>>>>>>>>>> ql.FlatForward(self.value_date, 0, >>>>>>>>>>>>>>>>>>> ql.Actual365Fixed())) >>>>>>>>>>>>>>>>>>> volatility = >>>>>>>>>>>>>>>>>>> ql.BlackVolTermStructureHandle(ql.BlackConstantVol( >>>>>>>>>>>>>>>>>>> self.value_date, ql.NullCalendar(), >>>>>>>>>>>>>>>>>>> self.volatility, ql.Actual365Fixed())) >>>>>>>>>>>>>>>>>>> initialValue = >>>>>>>>>>>>>>>>>>> ql.QuoteHandle(ql.SimpleQuote(self.underlyingPrice)) >>>>>>>>>>>>>>>>>>> process = ql.BlackScholesMertonProcess( >>>>>>>>>>>>>>>>>>> initialValue, dividendTS, riskFreeTS, >>>>>>>>>>>>>>>>>>> volatility) >>>>>>>>>>>>>>>>>>> return(process) >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> def evaluate(self, Option=None, >>>>>>>>>>>>>>>>>>> compute_implied_vol=None): >>>>>>>>>>>>>>>>>>> """ >>>>>>>>>>>>>>>>>>> Call the relevant option based on the input. >>>>>>>>>>>>>>>>>>> """ >>>>>>>>>>>>>>>>>>> self.riskFreeRate = self.riskFreeRate/100 >>>>>>>>>>>>>>>>>>> res = None >>>>>>>>>>>>>>>>>>> option = AsianOptionPrice.asian(self) >>>>>>>>>>>>>>>>>>> if compute_implied_vol == True: >>>>>>>>>>>>>>>>>>> # create Black Scholes process using IV. >>>>>>>>>>>>>>>>>>> process = AsianOptionPrice.bs_process( >>>>>>>>>>>>>>>>>>> self, create_process_for_iv=False, >>>>>>>>>>>>>>>>>>> use_IV=True) >>>>>>>>>>>>>>>>>>> engine = ql.FdBlackScholesAsianEngine( >>>>>>>>>>>>>>>>>>> process, tGrid=self.tGrid, >>>>>>>>>>>>>>>>>>> xGrid=self.xGrid, aGrid=self.aGrid) >>>>>>>>>>>>>>>>>>> option.setPricingEngine(engine) >>>>>>>>>>>>>>>>>>> res = {'value': option.NPV(), >>>>>>>>>>>>>>>>>>> 'impliedVolatility': self.IV} >>>>>>>>>>>>>>>>>>> else: >>>>>>>>>>>>>>>>>>> # Create Black Scholes process using HV. >>>>>>>>>>>>>>>>>>> process = AsianOptionPrice.bs_process( >>>>>>>>>>>>>>>>>>> self, create_process_for_iv=False, >>>>>>>>>>>>>>>>>>> use_IV=False) >>>>>>>>>>>>>>>>>>> engine = ql.FdBlackScholesAsianEngine( >>>>>>>>>>>>>>>>>>> process, tGrid=self.tGrid, >>>>>>>>>>>>>>>>>>> xGrid=self.xGrid, aGrid=self.aGrid) >>>>>>>>>>>>>>>>>>> option.setPricingEngine(engine) >>>>>>>>>>>>>>>>>>> res = {'value': option.NPV()} >>>>>>>>>>>>>>>>>>> return res >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> def evaluate_asian_npv(self): >>>>>>>>>>>>>>>>>>> Option = AsianOptionPrice.asian(self) >>>>>>>>>>>>>>>>>>> process = AsianOptionPrice.bs_process( >>>>>>>>>>>>>>>>>>> self, create_process_for_iv=False, >>>>>>>>>>>>>>>>>>> use_IV=False) >>>>>>>>>>>>>>>>>>> engine = ql.FdBlackScholesAsianEngine( >>>>>>>>>>>>>>>>>>> process, tGrid=self.tGrid, xGrid=self.xGrid, >>>>>>>>>>>>>>>>>>> aGrid=self.aGrid) >>>>>>>>>>>>>>>>>>> Option.setPricingEngine(engine) >>>>>>>>>>>>>>>>>>> res = Option.NPV() >>>>>>>>>>>>>>>>>>> return(res) >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> def calculate_greeks(self, Option=None, >>>>>>>>>>>>>>>>>>> compute_implied_vol=None): >>>>>>>>>>>>>>>>>>> today = self.value_date >>>>>>>>>>>>>>>>>>> Option = AsianOptionPrice.asian(self) >>>>>>>>>>>>>>>>>>> if compute_implied_vol == True: >>>>>>>>>>>>>>>>>>> process = AsianOptionPrice.bs_process( >>>>>>>>>>>>>>>>>>> self, create_process_for_iv=False, >>>>>>>>>>>>>>>>>>> use_IV=True) >>>>>>>>>>>>>>>>>>> else: >>>>>>>>>>>>>>>>>>> process = AsianOptionPrice.bs_process( >>>>>>>>>>>>>>>>>>> self, create_process_for_iv=False, >>>>>>>>>>>>>>>>>>> use_IV=False) >>>>>>>>>>>>>>>>>>> engine = ql.FdBlackScholesAsianEngine( >>>>>>>>>>>>>>>>>>> process, tGrid=self.tGrid, xGrid=self.xGrid, >>>>>>>>>>>>>>>>>>> aGrid=self.aGrid) >>>>>>>>>>>>>>>>>>> Option.setPricingEngine(engine) >>>>>>>>>>>>>>>>>>> option_greeks_ = {"delta": Option.delta(), >>>>>>>>>>>>>>>>>>> "gamma": Option.gamma( >>>>>>>>>>>>>>>>>>> )} >>>>>>>>>>>>>>>>>>> ArgmntList = AsianOptionPrice.create_arg_list( >>>>>>>>>>>>>>>>>>> self, Option, compute_implied_vol=True, >>>>>>>>>>>>>>>>>>> implied_vol_am_put=self.volatility) >>>>>>>>>>>>>>>>>>> DisturbedPrices = [] >>>>>>>>>>>>>>>>>>> arg_keys = ['Price', 'PRup', 'PVup', 'PTup'] >>>>>>>>>>>>>>>>>>> for i in range(4): >>>>>>>>>>>>>>>>>>> DisturbedPrices.append(eval( >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> "AsianOptionPrice(**ArgmntList[i]).evaluate_asian_npv()")) >>>>>>>>>>>>>>>>>>> print(DisturbedPrices) >>>>>>>>>>>>>>>>>>> PricesDistrbd = dict(zip(arg_keys, >>>>>>>>>>>>>>>>>>> DisturbedPrices)) >>>>>>>>>>>>>>>>>>> Rho = (PricesDistrbd['PRup'] - >>>>>>>>>>>>>>>>>>> PricesDistrbd['Price'])/(self.dist*100) >>>>>>>>>>>>>>>>>>> vega = (PricesDistrbd['PVup'] - >>>>>>>>>>>>>>>>>>> PricesDistrbd['Price'])/(self.dist*100) >>>>>>>>>>>>>>>>>>> thetaDay = (PricesDistrbd['PTup'] - >>>>>>>>>>>>>>>>>>> PricesDistrbd['Price']) >>>>>>>>>>>>>>>>>>> option_greeks_.update({'rho': Rho, 'vega': vega, >>>>>>>>>>>>>>>>>>> 'theta': thetaDay}) >>>>>>>>>>>>>>>>>>> return option_greeks_ >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> settings = { >>>>>>>>>>>>>>>>>>> "computeHistoricalVolatiltiy": False, >>>>>>>>>>>>>>>>>>> "tenantId": "380", >>>>>>>>>>>>>>>>>>> "gridPoints": 149, >>>>>>>>>>>>>>>>>>> "computeImpliedVolatiltiy": False, >>>>>>>>>>>>>>>>>>> "runId": 3735, >>>>>>>>>>>>>>>>>>> "timeSteps": 150, >>>>>>>>>>>>>>>>>>> "optionTrades": [ >>>>>>>>>>>>>>>>>>> { >>>>>>>>>>>>>>>>>>> "underlyingPrice": 98.69, >>>>>>>>>>>>>>>>>>> "refNo": "OPT-1135-GVA", >>>>>>>>>>>>>>>>>>> "underlyingPriceUnit": "US cents/LB", >>>>>>>>>>>>>>>>>>> "valueDate": "17-03-2022", >>>>>>>>>>>>>>>>>>> "fixingStartDate":"01-05-2022", >>>>>>>>>>>>>>>>>>> "pastFixings":0, >>>>>>>>>>>>>>>>>>> "volatility": 0.6015, >>>>>>>>>>>>>>>>>>> "openQuantity": 1, >>>>>>>>>>>>>>>>>>> "strikePriceUnit": "US cents/LB", >>>>>>>>>>>>>>>>>>> "optionType": "Call", >>>>>>>>>>>>>>>>>>> "expiryDate": "31-05-2022", >>>>>>>>>>>>>>>>>>> "exType": "asian", >>>>>>>>>>>>>>>>>>> "riskFreeRate": 0.8080374999999999, >>>>>>>>>>>>>>>>>>> "premium": 1, >>>>>>>>>>>>>>>>>>> "portfolioId": 18209, >>>>>>>>>>>>>>>>>>> "marketPremium": 11.42, >>>>>>>>>>>>>>>>>>> "longShort": "Long", >>>>>>>>>>>>>>>>>>> "optionInstrument": "EURO USD 1.105 Call", >>>>>>>>>>>>>>>>>>> "asset": "EURO USD 1.105 Call", >>>>>>>>>>>>>>>>>>> "portfolioName": "Option Trades", >>>>>>>>>>>>>>>>>>> "scenarioId": 8356, >>>>>>>>>>>>>>>>>>> "scenarioName": "Option Valuation - Greeks", >>>>>>>>>>>>>>>>>>> "strikePrice": 95, >>>>>>>>>>>>>>>>>>> "maturityInYears": 0.205479452 >>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>> ], >>>>>>>>>>>>>>>>>>> "userId": "5263", >>>>>>>>>>>>>>>>>>> "americanOptionsEngine": "CrankNicolson" >>>>>>>>>>>>>>>>>>> } >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> opt = AsianOptionPrice(**settings["optionTrades"][0]) >>>>>>>>>>>>>>>>>>> opt.evaluate_asian_npv() >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>> On Wed, May 18, 2022 at 9:25 PM Ashish Bansal < >>>>>>>>>>>>>>>>>>> ash...@gm...> wrote: >>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Hi Jack, >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Please find attached the Asian option python code and >>>>>>>>>>>>>>>>>>>> the option trade payload. >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> Thanks for your help, >>>>>>>>>>>>>>>>>>>> Regards, >>>>>>>>>>>>>>>>>>>> Ashish >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>> On Wed, 18 May 2022 at 12:12, Jack G < >>>>>>>>>>>>>>>>>>>> jac...@gm...> wrote: >>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Hi Ashish, >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> I've just lifted the examples from your references >>>>>>>>>>>>>>>>>>>>> directly for a discrete arithmetic Asian using the FD Asian engine and it >>>>>>>>>>>>>>>>>>>>> is working for me - can you share the code you are using that breaks please? >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> Best, >>>>>>>>>>>>>>>>>>>>> Jack >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> On Wed, 18 May 2022, 13:55 Ashish Bansal, < >>>>>>>>>>>>>>>>>>>>> ash...@gm...> wrote: >>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Hi all, >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Could somebody help with the asian options? We are >>>>>>>>>>>>>>>>>>>>>> using FdBlackScholesAsianengine with DiscreteAveragingAsianOption. Online >>>>>>>>>>>>>>>>>>>>>> references are given below. In DiscreteAveragingAsianOption, we need to >>>>>>>>>>>>>>>>>>>>>> pass the past fixing which is suggested to be 0 for new trade. Whereas when >>>>>>>>>>>>>>>>>>>>>> we are passing it as 0, we are getting the following error: >>>>>>>>>>>>>>>>>>>>>> "QuantLib Error - Running average requires at least >>>>>>>>>>>>>>>>>>>>>> one past fixing" >>>>>>>>>>>>>>>>>>>>>> ref: >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> https://quantlib-python-docs.readthedocs.io/en/latest/pricing_engines.html#fdblackscholesasianengine >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> https://quantlib-python-docs.readthedocs.io/en/latest/instruments/options.html?highlight=DiscreteAveragingAsianOption#ql.DiscreteAveragingAsianOption >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Queries: >>>>>>>>>>>>>>>>>>>>>> 1. What is to be passed here for new trade if not 0? >>>>>>>>>>>>>>>>>>>>>> 2. For trade which is mid of averaging and has few >>>>>>>>>>>>>>>>>>>>>> past fixings, do we need to pass the count of past fixing or the actual >>>>>>>>>>>>>>>>>>>>>> rates of the pastfixing as an array? >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Any help will be appreciated. >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> Thanks in advance >>>>>>>>>>>>>>>>>>>>>> Ashish >>>>>>>>>>>>>>>>>>>>>> _______________________________________________ >>>>>>>>>>>>>>>>>>>>>> QuantLib-users mailing list >>>>>>>>>>>>>>>>>>>>>> Qua...@li... >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users >>>>>>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>>>>> _______________________________________________ >>>>>>>>>>>>>>>>>> QuantLib-users mailing list >>>>>>>>>>>>>>>>>> Qua...@li... >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>>> https://lists.sourceforge.net/lists/listinfo/quantlib-users >>>>>>>>>>>>>>>>>> >>>>>>>>>>>>>>>>> |
