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Re: [Quantlib-users] CMS Spread Cap Pricing
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From: Peter C. <pca...@gm...> - 2022-08-15 18:18:47
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Hi Aditya, if I knew of an error in the code I would have fixed it already :-) As a first step I'd suggest to compare the adjusted CMS rates and compare to BBG if possible to see if we match their ATM level. As I said the CMS adjustment is dependent on the whole vol smile and if BBG uses a SABR model for vol extrapolation you won't match their vols outside ATM+-200 with a linearly interpolated cube. Thanks Peter On Mon, 15 Aug 2022 at 05:40, Aditya Gupta <adi...@co...> wrote: > Hi Peter, > > Regarding Bloomberg's model, it seems that it isn't very clear in their > documentation what engine is being used, but the Sabr cube is used for the > volatilities. I will try to contact support and ask them for more details. > > In any case, it seems that with a constant or non constant volatility, the > pricing is not accurate. What could be the error in the code? If Bloomberg > has a different model to the Quantlib one, the Quantlib model should still > be able to price within some reasonable error percentage. Since this is not > the case, where in the code could the mistake be? > > I am happy to send any updated code or fill in the missing lines. Thank > you for your time. > > Regards, > Aditya Gupta. > > On Aug 12, 2022 8:22 PM, Peter Caspers <pca...@gm...> wrote: > You don't often get email from pca...@gm.... Learn why this is > important <https://aka.ms/LearnAboutSenderIdentification> > CAUTION: EXTERNAL Sender > > Hi Aditya > > 2 the error shouldn't show up unless you pass a volatilityType to the > pricer. Might have to do with how SWIG calls the constructor, not sure at > the moment. > > 3 what I meant was: the cms price depends on the volatilities outside the > quoted part ATM-200 / ATM+200 as well, so maybe BBG uses different > extrapolated vols than you do. And as I said, the model itself (and the > mean reversion value) have a big impact, so you might first want check what > BBG is doing > > Thanks > Peter > > > > On Fri, 12 Aug 2022 at 10:43, Aditya Gupta <adi...@co...> > wrote: > >> Hi Peter, >> >> >> >> For point 2) the LinearTsrPricer can take the input as >> ql.ConstantSwaptionVolatilityStructure, but the Lognormal pricer complains >> by saying: " if only an atm surface is given, the volatility type must be >> inherited" So I guess it is not possible. To create a ‘constant volatility >> cube’, I instantiated an atmMatrix with a constant value, but all the OTM >> and ITM swaptions had a volatility of 0. This way, for every triplet >> (strike, expiry, tenor), QL would return a constant volatility. It is a >> workaround, but it sufficed for my purpose. >> >> >> >> For 3) The code and data for the extrapolation is below. Some notes: >> >> >> >> volHandle at the end is passed into the pricer. The data is not complete >> but should be enough for debugging purposes. The BBG/QL volatility cubes >> seem to match to a decent degree (i.e. I don’t believe it is the source of >> the error). The volatilities are within 10 bps when testing random data >> points. However, the atm strike does not change (even if the volatility is >> constant) and does not agree with Bloomberg. >> >> >> >> normalVolCube = >> >> Expiry >> >> 1Yr >> >> 2Yr >> >> 3Yr >> >> 4Yr >> >> 5Yr >> >> 7Yr >> >> 10Yr >> >> 12Yr >> >> 15Yr >> >> 20Yr >> >> 25Yr >> >> 30Yr >> >> 1Mo >> >> 153.65 >> >> 160.51 >> >> 151.81 >> >> 143.8 >> >> 141.03 >> >> 129.88 >> >> 123.16 >> >> 118.88 >> >> 110.61 >> >> 108.9 >> >> 104.21 >> >> 101.91 >> >> 3Mo >> >> 150.06 >> >> 152.12 >> >> 149.09 >> >> 140.08 >> >> 134.79 >> >> 125.74 >> >> 118.65 >> >> 115.21 >> >> 112.26 >> >> 107.13 >> >> 103.41 >> >> 99.43 >> >> 6Mo >> >> 154.30 >> >> 150.74 >> >> 143.9 >> >> 137.09 >> >> 132.84 >> >> 123.31 >> >> 115.03 >> >> 111.31 >> >> 107.88 >> >> 103.41 >> >> 100.5 >> >> 96.31 >> >> 9Mo >> >> 155.29 >> >> 149.86 >> >> 142.87 >> >> 135.17 >> >> 130.97 >> >> 121.33 >> >> 112.06 >> >> 108.57 >> >> 105.04 >> >> 99.95 >> >> 97.12 >> >> 93.19 >> >> 1Yr >> >> 155.98 >> >> 149.11 >> >> 140.78 >> >> 133.39 >> >> 127.82 >> >> 118.13 >> >> 109.42 >> >> 106.32 >> >> 102.06 >> >> 97.15 >> >> 94.05 >> >> 90.8 >> >> 2Yr >> >> 142.61 >> >> 135.71 >> >> 129.64 >> >> 123.34 >> >> 118.42 >> >> 110 >> >> 102.05 >> >> 99.13 >> >> 95.18 >> >> 89.95 >> >> 86.93 >> >> 84.38 >> >> 3Yr >> >> 130.05 >> >> 124.03 >> >> 117.71 >> >> 113.72 >> >> 109.87 >> >> 102.87 >> >> 96.37 >> >> 93.46 >> >> 89.5 >> >> 84.21 >> >> 81.15 >> >> 79.07 >> >> 4Yr >> >> 118.85 >> >> 113.22 >> >> 108.13 >> >> 104.87 >> >> 102.41 >> >> 97.49 >> >> 91.33 >> >> 88.78 >> >> 85.31 >> >> 79.42 >> >> 77.09 >> >> 74.93 >> >> 5Yr >> >> 109.73 >> >> 104.94 >> >> 102.2 >> >> 99.09 >> >> 96.85 >> >> 92.53 >> >> 87.38 >> >> 84.84 >> >> 81.37 >> >> 76.05 >> >> 73.36 >> >> 72.24 >> >> 6Yr >> >> 102.35 >> >> 99.42 >> >> 97.38 >> >> 94.42 >> >> 92.18 >> >> 88.36 >> >> 83.64 >> >> 81.19 >> >> 77.88 >> >> 72.88 >> >> 70.54 >> >> 69.33 >> >> 7Yr >> >> 96.52 >> >> 93.55 >> >> 91.44 >> >> 89.36 >> >> 87.41 >> >> 84.15 >> >> 79.99 >> >> 77.68 >> >> 74.58 >> >> 70.13 >> >> 68.25 >> >> 66.85 >> >> 8Yr >> >> 91.55 >> >> 89.03 >> >> 87.38 >> >> 85.58 >> >> 83.78 >> >> 80.83 >> >> 77.08 >> >> 74.82 >> >> 71.81 >> >> 67.42 >> >> 65.22 >> >> 64.37 >> >> 9Yr >> >> 86.91 >> >> 85.11 >> >> 83.57 >> >> 81.89 >> >> 80.19 >> >> 77.48 >> >> 74.07 >> >> 71.89 >> >> 68.99 >> >> 64.76 >> >> 62.82 >> >> 62.05 >> >> 10Yr >> >> 83.39 >> >> 81.29 >> >> 79.55 >> >> 77.84 >> >> 76.4 >> >> 74.03 >> >> 71.19 >> >> 69.2 >> >> 66.54 >> >> 62.56 >> >> 60.74 >> >> 60.1 >> >> 12Yr >> >> 77.24 >> >> 75.44 >> >> 74 >> >> 72.38 >> >> 70.97 >> >> 69.1 >> >> 66.47 >> >> 64.68 >> >> 62.27 >> >> 58.88 >> >> 57.3 >> >> 56.85 >> >> 15Yr >> >> 68.34 >> >> 67.24 >> >> 65.61 >> >> 64.58 >> >> 64.34 >> >> 62.11 >> >> 60.25 >> >> 58.73 >> >> 56.7 >> >> 53.89 >> >> 52.72 >> >> 52.28 >> >> 20Yr >> >> 60.48 >> >> 60.35 >> >> 59.15 >> >> 58.46 >> >> 57.75 >> >> 56.64 >> >> 55.07 >> >> 53.74 >> >> 51.97 >> >> 49.87 >> >> 48.96 >> >> 48.63 >> >> 25Yr >> >> 58.04 >> >> 56.62 >> >> 55.74 >> >> 55.16 >> >> 54.62 >> >> 53.59 >> >> 52.25 >> >> 51.07 >> >> 49.5 >> >> 47.49 >> >> 46.72 >> >> 46.08 >> >> 30Yr >> >> 54.83 >> >> 53.31 >> >> 52.73 >> >> 52.34 >> >> 51.96 >> >> 51.06 >> >> 49.7 >> >> 48.76 >> >> 47.52 >> >> 45.21 >> >> 44.11 >> >> 44.1 >> >> >> >> >> >> strikeSpreadData Snippet: >> >> Term x Tenor >> >> -200bps >> >> -100bps >> >> -50bps >> >> -25bps >> >> ATM >> >> 25bps >> >> 50bps >> >> 100bps >> >> 200bps >> >> 3Mo X 1Yr >> >> 163.06 >> >> 147.61 >> >> 145.65 >> >> 147.02 >> >> 150.06 >> >> 154.64 >> >> 160.47 >> >> 174.74 >> >> 208.13 >> >> 3Mo X 2Yr >> >> 168.32 >> >> 152.63 >> >> 149.59 >> >> 150.1 >> >> 152.12 >> >> 155.6 >> >> 160.36 >> >> 172.72 >> >> 203.32 >> >> 3Mo X 3Yr >> >> 165.94 >> >> 150.14 >> >> 146.87 >> >> 147.22 >> >> 149.09 >> >> 152.42 >> >> 157.05 >> >> 169.18 >> >> 199.38 >> >> 3Mo X 5Yr >> >> 154.44 >> >> 137.68 >> >> 133.49 >> >> 133.36 >> >> 134.79 >> >> 137.76 >> >> 142.09 >> >> 153.73 >> >> 182.89 >> >> 3Mo X 7Yr >> >> 145.98 >> >> 128.92 >> >> 124.5 >> >> 124.31 >> >> 125.74 >> >> 128.76 >> >> 133.16 >> >> 144.93 >> >> 173.96 >> >> 3Mo X 10Yr >> >> 141.64 >> >> 123.6 >> >> 118.33 >> >> 117.66 >> >> 118.65 >> >> 121.3 >> >> 125.42 >> >> 136.78 >> >> 165.14 >> >> 3Mo X 12Yr >> >> 124.87 >> >> 115.74 >> >> 113.96 >> >> 114.17 >> >> 115.21 >> >> 117.06 >> >> 119.67 >> >> 126.74 >> >> 145.33 >> >> 3Mo X 15Yr >> >> 137.53 >> >> 118.56 >> >> 112.58 >> >> 111.56 >> >> 112.26 >> >> 114.72 >> >> 118.73 >> >> 130 >> >> 158.17 >> >> 3Mo X 20Yr >> >> 133.96 >> >> 114.4 >> >> 107.91 >> >> 106.63 >> >> 107.13 >> >> 109.46 >> >> 113.42 >> >> 124.65 >> >> 152.72 >> >> 3Mo X 25Yr >> >> 130.47 >> >> 110.79 >> >> 104.19 >> >> 102.88 >> >> 103.41 >> >> 105.83 >> >> 109.9 >> >> 121.34 >> >> 149.56 >> >> 3Mo X 30Yr >> >> 126.66 >> >> 106.9 >> >> 100.19 >> >> 98.87 >> >> 99.43 >> >> 101.94 >> >> 106.13 >> >> 117.77 >> >> 146.12 >> >> 6Mo X 1Yr >> >> 153.08 >> >> 147.26 >> >> 148.88 >> >> 151.13 >> >> 154.3 >> >> 158.3 >> >> 163.02 >> >> 174.1 >> >> 199.93 >> >> 6Mo X 2Yr >> >> 155.61 >> >> 147.49 >> >> 147.3 >> >> 148.56 >> >> 150.74 >> >> 153.82 >> >> 157.69 >> >> 167.34 >> >> 191.18 >> >> 6Mo X 3Yr >> >> 151.41 >> >> 141.74 >> >> 140.85 >> >> 141.85 >> >> 143.9 >> >> 146.93 >> >> 150.84 >> >> 160.73 >> >> 185.27 >> >> 6Mo X 5Yr >> >> 144.96 >> >> 132.89 >> >> 130.7 >> >> 131.19 >> >> 132.84 >> >> 135.64 >> >> 139.46 >> >> 149.43 >> >> 174.44 >> >> 6Mo X 7Yr >> >> 137.51 >> >> 124.48 >> >> 121.67 >> >> 121.87 >> >> 123.31 >> >> 125.95 >> >> 129.67 >> >> 139.51 >> >> 164.24 >> >> 6Mo X 10Yr >> >> 131.51 >> >> 117.49 >> >> 113.98 >> >> 113.86 >> >> 115.03 >> >> 117.47 >> >> 121.05 >> >> 130.73 >> >> 155.14 >> >> 6Mo X 12Yr >> >> 119.63 >> >> 111.38 >> >> 109.93 >> >> 110.24 >> >> 111.31 >> >> 113.15 >> >> 115.68 >> >> 122.45 >> >> 140.14 >> >> 6Mo X 15Yr >> >> 126.39 >> >> 111.49 >> >> 107.36 >> >> 106.95 >> >> 107.88 >> >> 110.16 >> >> 113.64 >> >> 123.22 >> >> 147.39 >> >> 6Mo X 20Yr >> >> 123.32 >> >> 107.85 >> >> 103.29 >> >> 102.66 >> >> 103.41 >> >> 105.58 >> >> 109 >> >> 118.53 >> >> 142.61 >> >> 6Mo X 25Yr >> >> 120.86 >> >> 105.2 >> >> 100.48 >> >> 99.78 >> >> 100.5 >> >> 102.67 >> >> 106.1 >> >> 115.7 >> >> 139.82 >> >> >> >> >> >> # ATM Volatility matrix >> >> volType = ql.Normal >> >> flatExtrapolation = False >> >> >> >> atmSwapTenors = [ql.Period(tenor[:-1]) for tenor in >> normalVolCube.columns[1:]] >> >> atmOptionTenors = [ql.Period(tenor[:-1]) for tenor in >> normalVolCube['Expiry']] >> >> >> >> normalVols = normalVolCube[normalVolCube.columns[1:]].apply( >> >> lambda x: x * 1e-4).values.tolist() >> >> >> >> swaptionVolMatrix = ql.SwaptionVolatilityMatrix(calendar, convention, >> >> atmOptionTenors, >> atmSwapTenors, >> >> ql.Matrix(normalVols), >> >> dayCounter, >> flatExtrapolation, >> >> volType) >> >> >> >> swaptionVolMatrixHandle = ql.SwaptionVolatilityStructureHandle( >> >> swaptionVolMatrix) >> >> >> >> strikeSpreads = [-200, -100, -50, -25, 0, 25, 50, 100, 200] >> >> strikeSpreads = [x * 1e-4 for x in strikeSpreads] >> >> >> >> optionTenors = [ >> >> ql.Period(3, ql.Months), >> >> ql.Period(6, ql.Months), >> >> ql.Period(9, ql.Months), >> >> ql.Period(1, ql.Years), >> >> ql.Period(3, ql.Years), >> >> ql.Period(5, ql.Years), >> >> ql.Period(7, ql.Years), >> >> ql.Period(10, ql.Years), >> >> ql.Period(15, ql.Years), >> >> ql.Period(20, ql.Years), >> >> ql.Period(30, ql.Years) >> >> ] >> >> >> >> swapTenors = [ >> >> ql.Period(1, ql.Years), >> >> ql.Period(2, ql.Years), >> >> ql.Period(3, ql.Years), >> >> ql.Period(5, ql.Years), >> >> ql.Period(7, ql.Years), >> >> ql.Period(10, ql.Years), >> >> ql.Period(12, ql.Years), >> >> ql.Period(15, ql.Years), >> >> ql.Period(20, ql.Years), >> >> ql.Period(25, ql.Years), >> >> ql.Period(30, ql.Years) >> >> ] >> >> >> >> nRows = len(optionTenors) * len(swapTenors) >> >> nCols = len(strikeSpreads) >> >> >> >> volSpreadsMatrix = strikeSpreadData[strikeSpreadData.columns[1:]].apply( >> >> lambda x: x).values.tolist() >> >> >> >> volSpreads = [] >> >> >> >> for i in range(nRows): >> >> volSpreadsRow = [] >> >> for j in range(nCols): >> >> volSpreadsRow.append( >> >> ql.QuoteHandle(ql.SimpleQuote(volSpreadsMatrix[i][j]))) >> >> >> >> volSpreads.append(volSpreadsRow) >> >> >> >> vegaWeightedSmileFit = False >> >> >> >> volCube = ql.SwaptionVolCube2(swaptionVolMatrixHandle, optionTenors, >> >> swapTenors, strikeSpreads, volSpreads, >> >> swapIndex1, swapIndex2, >> vegaWeightedSmileFit) >> >> >> >> volHandle = ql.SwaptionVolatilityStructureHandle(volCube) >> >> volHandle.enableExtrapolation() >> >> >> >> Thank you! >> >> >> >> -----Original Message----- >> From: Peter Caspers <pca...@gm...> >> Sent: Friday, August 12, 2022 4:20 PM >> To: Aditya Gupta <adi...@co...> >> Cc: qua...@li... >> Subject: Re: [Quantlib-users] CMS Spread Cap Pricing >> >> >> >> [You don't often get email from pca...@gm.... Learn why this >> is important at https://aka.ms/LearnAboutSenderIdentification ] >> >> >> >> CAUTION: EXTERNAL Sender >> >> >> >> >> >> Hi Aditya >> >> >> >> 1. correct >> >> 2. the pricer takes any SwaptionVolatilityStructure, i.e. a >> ConstantSwaptionVolatilityStructure should work. Is that what you mean? >> >> 3. this might be related to smile extrapolation, which strike range do >> you cover in your swaption cube and how do you set up the extrapolation? >> The pricer uses an integration over [-200%, +200%] by default. >> >> 4. I agree 500% is much to high, let's discuss further once 3 is >> resolved, i.e. QL / BBG matches >> >> >> >> In addition, the linear TSR model might not work well when the time to >> expiry is large. Do you have more info on which model BBG uses? >> >> >> >> Thank you >> >> Peter >> >> >> >> On Fri, 12 Aug 2022 at 09:36, Aditya Gupta <adi...@co...> >> wrote: >> >> > >> >> > Hi, >> >> > >> >> > >> >> > >> >> > I am trying to price a CMS Spread Cap using Quantlib. I am using data >> from Bloomberg, and I am using the following classes: >> >> > >> >> > >> >> > >> >> > yts is a bootstrapped Sofr curve >> >> > >> >> > Instrument = ql.CappedFlooredCmsSpreadCoupon() >> >> > >> >> > volCube = ql.SwaptionVolCube2() with the correct data and volType = >> >> > ql.Normal >> >> > >> >> > cmsPricer = ql.LinearTSRPricer(volHandle, meanReversion, yts) >> >> > >> >> > pricer = ql.LognormalCmsSpreadPricer(cmsPricer, correlation, yts, 16) >> >> > >> >> > >> >> > >> >> > I have a few questions. >> >> > >> >> > >> >> > >> >> > It seems to me that to price a ql. CappedFlooredCmsSpreadCoupon, the >> only option is to use a ql.LognormalCmsSpreadPricer, and hence if we use >> Normal volatility, the input cmsPricer must be the LinearTSRPricer (Hagan >> cannot be used). Is my assessment correct? >> >> > In order to check the pricing, I used a ‘constant’ volatility cube (set >> the atm matrix to a constant, rest to 0) and tested the pricing. The >> instrument was priced correctly (within the error from the discount curve). >> Can the pricer accept a constant volatility otherwise? >> >> > The volCube seems to be returning the correct volatilities (within >> reason) when compared to Bloomberg, and is being passed to the pricer >> without error. But for some reason, the price is completely different >> (about half of what it should be). What could be the cause for the error, >> or how could I go about debugging this? >> >> > If I use scipy.optimize to ‘artificially’ optimize the price as a >> function of the mean reversion, for a value of around 5 (which is way too >> high for the mean correlation), I was able to bring the error down to less >> than 1 dollar. Clearly this approach is not sensible. So I used the >> HullWhite model on the swaption matrix, and calibrated the mean reversion. >> But the error for this value of the mean reversion is much too high. How >> can I determine a good value for the mean reversion? >> >> > >> >> > >> >> > >> >> > Right now I am content with there being some error compared to >> Bloomberg, but I am unable to understand the blackbox behind the pricing >> engine and the mean reversion calibration, and if there is any way to solve >> these issues. Any and all help would be greatly appreciated. Thank you. >> >> > >> >> > >> >> > >> >> > Some of the code and data is as follows: >> >> > >> >> > >> >> > >> >> > today = ql.Date(25, 7, 2022) >> >> > >> >> > ql.Settings.instance().evaluationDate = today >> >> > >> >> > >> >> > >> >> > #Global variables >> >> > >> >> > calendar = ql.UnitedStates(ql.UnitedStates.FederalReserve) >> >> > >> >> > convention = ql.ModifiedFollowing >> >> > >> >> > endOfMonth = False >> >> > >> >> > dayCounter = ql.Actual360() >> >> > >> >> > fixingDays = 0 >> >> > >> >> > compounding = ql.Compounded >> >> > >> >> > compoundingFrequency = ql.Annual >> >> > >> >> > >> >> > >> >> > yts = ql.RelinkableYieldTermStructureHandle() >> >> > >> >> > index = ql.Sofr(yts) >> >> > >> >> > >> >> > >> >> > helper = [] >> >> > >> >> > >> >> > >> >> > # sofrData are just the rates from a dataframe >> >> > >> >> > >> >> > >> >> > helper += [ >> >> > >> >> > ql.DepositRateHelper( >> >> > >> >> > ql.QuoteHandle(ql.SimpleQuote(sofrData['Shifted Rate'][0] / >> >> > 100.0)), >> >> > >> >> > index) >> >> > >> >> > ] >> >> > >> >> > >> >> > >> >> > helper += [ >> >> > >> >> > ql.OISRateHelper(0, >> >> > >> >> > ql.Period(expiration), >> >> > >> >> > ql.QuoteHandle(ql.SimpleQuote(rate / 100.0)), >> >> > >> >> > index, >> >> > >> >> > paymentLag=0, >> >> > >> >> > paymentConvention=convention, >> >> > >> >> > paymentFrequency=ql.Annual, >> >> > >> >> > paymentCalendar=calendar, >> >> > >> >> > averagingMethod=ql.RateAveraging.Compound) for >> >> > rate, >> >> > >> >> > expiration in zip(sofrData['Shifted Rate'][1:], >> >> > sofrData["Term"][1:]) >> >> > >> >> > ] >> >> > >> >> > >> >> > >> >> > curve = ql.PiecewiseSplineCubicDiscount(today, helper, ql.Actual360()) >> >> > >> >> > curve.enableExtrapolation() >> >> > >> >> > yts.linkTo(curve) >> >> > >> >> > >> >> > >> >> > meanReversion = ql.QuoteHandle(ql.SimpleQuote(5.037702498779181)) >> >> > #this is the ‘optimised’ value but I need further help >> >> > >> >> > correlation = ql.QuoteHandle(ql.SimpleQuote(36.6 * 1e-4)) >> >> > >> >> > >> >> > >> >> > nominal = 1e9 >> >> > >> >> > gearing = 1.0 >> >> > >> >> > spread = 0.0 >> >> > >> >> > cap = -8 * 1e-4 >> >> > >> >> > floor = ql.nullDouble() #infinite floor >> >> > >> >> > >> >> > >> >> > startDate = ql.Date(26, 9, 2022) >> >> > >> >> > endDate = ql.Date(26, 9, 2023) >> >> > >> >> > paymentDate = ql.Date(26, 9, 2023) >> >> > >> >> > >> >> > >> >> > isInArrears = False >> >> > >> >> > exCouponDate = ql.Date() >> >> > >> >> > >> >> > >> >> > fixingDays = 0 >> >> > >> >> > >> >> > >> >> > swapIndex1 = ql.UsdLiborSwapIsdaFixAm(ql.Period('30y'), yts, yts) >> >> > >> >> > ql.IndexManager.instance().clearHistory(swapIndex1.name()) >> >> > >> >> > >> >> > >> >> > swapIndex2 = ql.UsdLiborSwapIsdaFixAm(ql.Period('10y'), yts, yts) >> >> > >> >> > ql.IndexManager.instance().clearHistory(swapIndex2.name()) >> >> > >> >> > >> >> > >> >> > spreadIndex = ql.SwapSpreadIndex("Joint Index", swapIndex1, >> >> > swapIndex2) >> >> > >> >> > >> >> > >> >> > instrument = ql.CappedFlooredCmsSpreadCoupon(paymentDate, nominal, >> >> > startDate, >> >> > >> >> > endDate, fixingDays, >> >> > spreadIndex, >> >> > >> >> > gearing, spread, cap, >> >> > floor, >> >> > >> >> > ql.Date(), ql.Date(), >> >> > dayCounter, >> >> > >> >> > isInArrears, >> >> > exCouponDate) >> >> > >> >> > >> >> > >> >> > cmsPricer = ql.LinearTsrPricer(volHandle, meanReversion, yts) >> >> > >> >> > >> >> > >> >> > # cmsPricer = ql.NumericHaganPricer(volHandle, >> >> > ql.GFunctionFactory.ParallelShifts , meanReversion)#, Rate >> >> > lowerLimit=0.0, Rate upperLimit=1.0, Real precision=1.0e-6, Real >> >> > hardUpperLimit=QL_MAX_REAL) >> >> > >> >> > >> >> > >> >> > integrationPoints = 16 >> >> > >> >> > >> >> > >> >> > pricer = ql.LognormalCmsSpreadPricer(cmsPricer, correlation, yts, >> >> > >> >> > integrationPoints) >> >> > >> >> > >> >> > >> >> > instrument.setPricer(pricer) >> >> > >> >> > >> >> > >> >> > Regards, >> >> > >> >> > Aditya Gupta. >> >> > >> >> > >> >> > >> >> > ________________________________ >> >> > Complus Asset Management Limited ("CAML") is licensed by the Securities >> and Futures Commission of Hong Kong (“SFC”) for the Regulated Activities of >> Advising on Securities and Asset Management with CE number AWX256. CAML is >> subject to certain SFC codes and regulations, details of which can be found >> on the SFC's website at http://www.sfc.hk. CAML is a company >> incorporated in Hong Kong with its registered office and principal place of >> business as indicated above. >> >> > >> >> > Unless specifically indicated, this message should not be construed as >> an offer to sell or solicitation to purchase any securities, financial >> products or services or the giving of investment advice within or outside >> Hong Kong. This message is intended solely for the person(s) to whom it is >> addressed. If you receive this message in error, please immediately delete >> it and all copies of it from your computer network or hard copies, and >> notify the sender. >> >> > _______________________________________________ >> >> > QuantLib-users mailing list >> >> > Qua...@li... >> >> > https://lists.sourceforge.net/lists/listinfo/quantlib-users >> > |
