def _blsimpv(price, spot, strike, risk_free_rate, time, option_type, dividend): spot = SimpleQuote(spot) daycounter = ActualActual(ISMA) risk_free_ts = FlatForward(today(), risk_free_rate, daycounter) dividend_ts = FlatForward(today(), dividend, daycounter) volatility_ts = BlackConstantVol(today(), NullCalendar(), .3, daycounter) process = BlackScholesMertonProcess(spot, dividend_ts, risk_free_ts, volatility_ts) exercise_date = today() + Period(time * 365, Days) exercise = EuropeanExercise(exercise_date) payoff = PlainVanillaPayoff(option_type, strike) option = EuropeanOption(payoff, exercise) engine = AnalyticEuropeanEngine(process) option.set_pricing_engine(engine) accuracy = 0.001 max_evaluations = 1000 min_vol = 0.01 max_vol = 2 vol = option.implied_volatility(price, process, accuracy, max_evaluations, min_vol, max_vol) return vol
def _blsprice(spot, strike, risk_free_rate, time, volatility, option_type='Call', dividend=0.0, calc='price'): """ Black-Scholes option pricing model + greeks. """ _spot = SimpleQuote(spot) daycounter = ActualActual(ISMA) risk_free_ts = FlatForward(today(), risk_free_rate, daycounter) dividend_ts = FlatForward(today(), dividend, daycounter) volatility_ts = BlackConstantVol(today(), NullCalendar(), volatility, daycounter) process = BlackScholesMertonProcess(_spot, dividend_ts, risk_free_ts, volatility_ts) exercise_date = today() + Period(time * 365, Days) exercise = EuropeanExercise(exercise_date) payoff = PlainVanillaPayoff(option_type, strike) option = EuropeanOption(payoff, exercise) engine = AnalyticEuropeanEngine(process) option.set_pricing_engine(engine) if calc == 'price': res = option.npv elif calc == 'delta': res = option.delta elif calc == 'gamma': res = option.gamma elif calc == 'theta': res = option.theta elif calc == 'rho': res = option.rho elif calc == 'vega': res = option.vega elif calc == 'lambda': res = option.delta * spot / option.npv else: raise ValueError('calc type %s is unknown' % calc) return res
def blsprice(spot, strike, risk_free_rate, time, volatility, option_type='Call', dividend=0.0): """ """ spot = SimpleQuote(spot) daycounter = Actual360() risk_free_ts = FlatForward(today(), risk_free_rate, daycounter) dividend_ts = FlatForward(today(), dividend, daycounter) volatility_ts = BlackConstantVol(today(), NullCalendar(), volatility, daycounter) process = BlackScholesMertonProcess(spot, dividend_ts, risk_free_ts, volatility_ts) exercise_date = today() + 90 exercise = EuropeanExercise(exercise_date) payoff = PlainVanillaPayoff(option_type, strike) option = EuropeanOption(payoff, exercise) engine = AnalyticEuropeanEngine(process) option.set_pricing_engine(engine) return option.npv
def dividendOption(): # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # ++++++++++++++++++++ General Parameter for all the computation +++++++++++++++++++++++ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # declaration of the today's date (date where the records are done) todaysDate = Date(24, Jan, 2012) # INPUT Settings.instance( ).evaluation_date = todaysDate #!\ IMPORTANT COMMAND REQUIRED FOR ALL VALUATIONS calendar = UnitedStates() # INPUT settlement_days = 2 # INPUT # Calcul of the settlement date : need to add a period of 2 days to the todays date settlementDate = calendar.advance(todaysDate, period=Period(settlement_days, Days)) dayCounter = Actual360() # INPUT currency = USDCurrency() # INPUT print("Date of the evaluation: ", todaysDate) print("Calendar used: ", calendar.name) print("Number of settlement Days: ", settlement_days) print("Date of settlement: ", settlementDate) print("Convention of day counter: ", dayCounter.name()) print("Currency of the actual context:\t\t", currency.name) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # ++++++++++++++++++++ Description of the underlying +++++++++++++++++++++++++++++++++++ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ underlying_name = "IBM" underlying_price = 191.75 # INPUT underlying_vol = 0.2094 # INPUT print("**********************************") print("Name of the underlying: ", underlying_name) print("Price of the underlying at t0: ", underlying_price) print("Volatility of the underlying: ", underlying_vol) # For a great managing of price and vol objects --> Handle underlying_priceH = SimpleQuote(underlying_price) # We suppose the vol constant : his term structure is flat --> BlackConstantVol object flatVolTS = BlackConstantVol(settlementDate, calendar, underlying_vol, dayCounter) # ++++++++++++++++++++ Description of Yield Term Structure # Libor data record print("**********************************") print("Description of the Libor used for the Yield Curve construction") Libor_dayCounter = Actual360() liborRates = [] liborRatesTenor = [] # INPUT : all the following data are input : the rate and the corresponding tenor # You could make the choice of more or less data # --> However you have tho choice the instruments with different maturities liborRates = [ 0.002763, 0.004082, 0.005601, 0.006390, 0.007125, 0.007928, 0.009446, 0.01110 ] liborRatesTenor = [ Period(tenor, Months) for tenor in [1, 2, 3, 4, 5, 6, 9, 12] ] for tenor, rate in zip(liborRatesTenor, liborRates): print(tenor, "\t\t\t", rate) # Swap data record # description of the fixed leg of the swap Swap_fixedLegTenor = Period(12, Months) # INPUT Swap_fixedLegConvention = ModifiedFollowing # INPUT Swap_fixedLegDayCounter = Actual360() # INPUT # description of the float leg of the swap Swap_iborIndex = Libor("USDLibor", Period(3, Months), settlement_days, USDCurrency(), UnitedStates(), Actual360()) print("Description of the Swap used for the Yield Curve construction") print("Tenor of the fixed leg: ", Swap_fixedLegTenor) print("Index of the floated leg: ", Swap_iborIndex.name) print("Maturity Rate ") swapRates = [] swapRatesTenor = [] # INPUT : all the following data are input : the rate and the corresponding tenor # You could make the choice of more or less data # --> However you have tho choice the instruments with different maturities swapRates = [ 0.005681, 0.006970, 0.009310, 0.012010, 0.014628, 0.016881, 0.018745, 0.020260, 0.021545 ] swapRatesTenor = [Period(i, Years) for i in range(2, 11)] for tenor, rate in zip(swapRatesTenor, swapRates): print(tenor, "\t\t\t", rate) # ++++++++++++++++++++ Creation of the vector of RateHelper (need for the Yield Curve construction) # ++++++++++++++++++++ Libor LiborFamilyName = currency.name + "Libor" instruments = [] for rate, tenor in zip(liborRates, liborRatesTenor): # Index description ___ creation of a Libor index liborIndex = Libor(LiborFamilyName, tenor, settlement_days, currency, calendar, Libor_dayCounter) # Initialize rate helper ___ the DepositRateHelper link the recording rate with the Libor index instruments.append(DepositRateHelper(rate, index=liborIndex)) # +++++++++++++++++++++ Swap SwapFamilyName = currency.name + "swapIndex" for tenor, rate in zip(swapRatesTenor, swapRates): # swap description ___ creation of a swap index. The floating leg is described in the index 'Swap_iborIndex' swapIndex = SwapIndex(SwapFamilyName, tenor, settlement_days, currency, calendar, Swap_fixedLegTenor, Swap_fixedLegConvention, Swap_fixedLegDayCounter, Swap_iborIndex) # Initialize rate helper __ the SwapRateHelper links the swap index width his rate instruments.append(SwapRateHelper.from_index(rate, swapIndex)) # ++++++++++++++++++ Now the creation of the yield curve riskFreeTS = PiecewiseYieldCurve.from_reference_date( BootstrapTrait.ZeroYield, Interpolator.Linear, settlementDate, instruments, dayCounter) # ++++++++++++++++++ build of the underlying process : with a Black-Scholes model print('Creating process') bsProcess = BlackScholesProcess(underlying_priceH, riskFreeTS, flatVolTS) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # ++++++++++++++++++++ Description of the option +++++++++++++++++++++++++++++++++++++++ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Option_name = "IBM Option" maturity = Date(26, Jan, 2013) strike = 190 option_type = 'call' # Here, as an implementation exemple, we make the test with borth american and european exercise europeanExercise = EuropeanExercise(maturity) # The emericanExercise need also the settlement date, as his right to exerce the buy or call start at the settlement date! #americanExercise = AmericanExercise(settlementDate, maturity) americanExercise = AmericanExercise(maturity, settlementDate) print("**********************************") print("Description of the option: ", Option_name) print("Date of maturity: ", maturity) print("Type of the option: ", option_type) print("Strike of the option: ", strike) # ++++++++++++++++++ Description of the discrete dividends # INPUT You have to determine the frequece and rates of the discrete dividend. Here is a sollution, but she's not the only one. # Last know dividend: dividend = 0.75 #//0.75 next_dividend_date = Date(10, Feb, 2012) # HERE we have make the assumption that the dividend will grow with the quarterly croissance: dividendCroissance = 1.03 dividendfrequence = Period(3, Months) dividendDates = [] dividends = [] d = next_dividend_date while d <= maturity: dividendDates.append(d) dividends.append(dividend) d = d + dividendfrequence dividend *= dividendCroissance print("Discrete dividends ") print("Dates Dividends ") for date, div in zip(dividendDates, dividends): print(date, " ", div) # ++++++++++++++++++ Description of the final payoff payoff = PlainVanillaPayoff(option_type, strike) # ++++++++++++++++++ The OPTIONS : (American and European) with their dividends description: dividendEuropeanOption = DividendVanillaOption(payoff, europeanExercise, dividendDates, dividends) dividendAmericanOption = DividendVanillaOption(payoff, americanExercise, dividendDates, dividends) # just too test europeanOption = VanillaOption(payoff, europeanExercise) americanOption = VanillaOption(payoff, americanExercise) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # ++++++++++++++++++++ Description of the pricing +++++++++++++++++++++++++++++++++++++ # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # For the european options we have a closed analytic formula: The Black Scholes: dividendEuropeanEngine = AnalyticDividendEuropeanEngine(bsProcess) # For the american option we have make the choice of the finite difference model with the CrankNicolson scheme # this model need to precise the time and space step # More they are greater, more the calul will be precise. americanGirdPoints = 600 americanTimeSteps = 600 dividendAmericanEngine = FDDividendAmericanEngine('CrankNicolson', bsProcess, americanTimeSteps, americanGirdPoints) # just to test europeanEngine = AnalyticEuropeanEngine(bsProcess) americanEngine = FDAmericanEngine('CrankNicolson', bsProcess, americanTimeSteps, americanGirdPoints) # ++++++++++++++++++++ Valorisation ++++++++++++++++++++++++++++++++++++++++ # Link the pricing Engine to the option dividendEuropeanOption.set_pricing_engine(dividendEuropeanEngine) dividendAmericanOption.set_pricing_engine(dividendAmericanEngine) # just to test europeanOption.set_pricing_engine(europeanEngine) americanOption.set_pricing_engine(americanEngine) # Now we make all the needing calcul # ... and final results print( "NPV of the European Option with discrete dividends=0: {:.4f}".format( dividendEuropeanOption.npv)) print("NPV of the European Option without dividend: {:.4f}".format( europeanOption.npv)) print( "NPV of the American Option with discrete dividends=0: {:.4f}".format( dividendAmericanOption.npv)) print("NPV of the American Option without dividend: {:.4f}".format( americanOption.npv)) # just a single test print("ZeroRate with a maturity at ", maturity, ": ", \ riskFreeTS.zero_rate(maturity, dayCounter, Simple))
def test_bsm_hw(self): print("Testing European option pricing for a BSM process" + " with one-factor Hull-White model...") dc = Actual365Fixed() todays_date = today() maturity_date = todays_date + Period(20, Years) settings = Settings() settings.evaluation_date = todays_date spot = SimpleQuote(100) q_ts = flat_rate(todays_date, 0.04, dc) r_ts = flat_rate(todays_date, 0.0525, dc) vol_ts = BlackConstantVol(todays_date, NullCalendar(), 0.25, dc) hullWhiteModel = HullWhite(r_ts, 0.00883, 0.00526) bsm_process = BlackScholesMertonProcess(spot, q_ts, r_ts, vol_ts) exercise = EuropeanExercise(maturity_date) fwd = spot.value * q_ts.discount(maturity_date) / \ r_ts.discount(maturity_date) payoff = PlainVanillaPayoff(Call, fwd) option = VanillaOption(payoff, exercise) tol = 1e-8 corr = [-0.75, -0.25, 0.0, 0.25, 0.75] expectedVol = [ 0.217064577, 0.243995801, 0.256402830, 0.268236596, 0.290461343 ] for c, v in zip(corr, expectedVol): bsm_hw_engine = AnalyticBSMHullWhiteEngine(c, bsm_process, hullWhiteModel) option = VanillaOption(payoff, exercise) option.set_pricing_engine(bsm_hw_engine) npv = option.npv compVolTS = BlackConstantVol(todays_date, NullCalendar(), v, dc) bs_process = BlackScholesMertonProcess(spot, q_ts, r_ts, compVolTS) bsEngine = AnalyticEuropeanEngine(bs_process) comp = VanillaOption(payoff, exercise) comp.set_pricing_engine(bsEngine) impliedVol = comp.implied_volatility(npv, bs_process, 1e-10, 500, min_vol=0.1, max_vol=0.4) if (abs(impliedVol - v) > tol): print("Failed to reproduce implied volatility cor: %f" % c) print("calculated: %f" % impliedVol) print("expected : %f" % v) if abs((comp.npv - npv) / npv) > tol: print("Failed to reproduce NPV") print("calculated: %f" % comp.npv) print("expected : %f" % npv) self.assertAlmostEqual(impliedVol, v, delta=tol) self.assertAlmostEqual(comp.npv / npv, 1, delta=tol)
def test_compare_bsm_bsmhw_hestonhw(self): dc = Actual365Fixed() todays_date = today() settings = Settings() settings.evaluation_date = todays_date tol = 1.e-2 spot = SimpleQuote(100) dates = [todays_date + Period(i, Years) for i in range(40)] rates = [0.01 + 0.0002 * np.exp(np.sin(i / 4.0)) for i in range(40)] divRates = [0.02 + 0.0001 * np.exp(np.sin(i / 5.0)) for i in range(40)] s0 = SimpleQuote(100) r_ts = ZeroCurve(dates, rates, dc) q_ts = ZeroCurve(dates, divRates, dc) vol = SimpleQuote(0.25) vol_ts = BlackConstantVol(todays_date, NullCalendar(), vol.value, dc) bsm_process = BlackScholesMertonProcess(spot, q_ts, r_ts, vol_ts) payoff = PlainVanillaPayoff(Call, 100) exercise = EuropeanExercise(dates[1]) option = VanillaOption(payoff, exercise) analytic_european_engine = AnalyticEuropeanEngine(bsm_process) option.set_pricing_engine(analytic_european_engine) npv_bsm = option.npv variance = vol.value * vol.value hestonProcess = HestonProcess(risk_free_rate_ts=r_ts, dividend_ts=q_ts, s0=s0, v0=variance, kappa=5.0, theta=variance, sigma=1e-4, rho=0.0) hestonModel = HestonModel(hestonProcess) hullWhiteModel = HullWhite(r_ts, a=0.01, sigma=0.01) bsmhwEngine = AnalyticBSMHullWhiteEngine(0.0, bsm_process, hullWhiteModel) hestonHwEngine = AnalyticHestonHullWhiteEngine(hestonModel, hullWhiteModel, 128) hestonEngine = AnalyticHestonEngine(hestonModel, 144) option.set_pricing_engine(hestonEngine) npv_heston = option.npv option.set_pricing_engine(bsmhwEngine) npv_bsmhw = option.npv option.set_pricing_engine(hestonHwEngine) npv_hestonhw = option.npv print("calculated with BSM: %f" % npv_bsm) print("BSM-HW: %f" % npv_bsmhw) print("Heston: %f" % npv_heston) print("Heston-HW: %f" % npv_hestonhw) self.assertAlmostEqual(npv_bsm, npv_bsmhw, delta=tol) self.assertAlmostEqual(npv_bsm, npv_hestonhw, delta=tol)
# bootstrap the yield/dividend/vol curves flat_term_structure = FlatForward(reference_date=settlement_date, forward=risk_free_rate, daycounter=daycounter) flat_dividend_ts = FlatForward(reference_date=settlement_date, forward=dividend_yield, daycounter=daycounter) flat_vol_ts = BlackConstantVol(settlement_date, calendar, volatility, daycounter) black_scholes_merton_process = BlackScholesMertonProcess( underlyingH, flat_dividend_ts, flat_term_structure, flat_vol_ts) payoff = PlainVanillaPayoff(option_type, strike) european_exercise = EuropeanExercise(maturity) european_option = VanillaOption(payoff, european_exercise) method = 'Black-Scholes' analytic_european_engine = AnalyticEuropeanEngine(black_scholes_merton_process) european_option.set_pricing_engine(analytic_european_engine) print('today: %s settlement: %s maturity: %s' % (todays_date, settlement_date, maturity)) print('NPV: %f\n' % european_option.net_present_value) ### EOF #######################################################################