def test_smith(self): # test against result published in # Journal of Computational Finance Vol. 11/1 Fall 2007 # An almost exact simulation method for the heston model settlement_date = today() self.settings.evaluation_date = settlement_date daycounter = ActualActual() timeToMaturity = 4 exercise_date = settlement_date + timeToMaturity * 365 c_payoff = PlainVanillaPayoff(Call, 100) exercise = EuropeanExercise(exercise_date) risk_free_ts = flat_rate(0., daycounter) dividend_ts = flat_rate(0., daycounter) s0 = SimpleQuote(100.0) v0 = 0.0194 kappa = 1.0407 theta = 0.0586 sigma = 0.5196 rho = -.6747 nb_steps_a = 100 nb_paths = 20000 seed = 12347 process = HestonProcess( risk_free_ts, dividend_ts, s0, v0, kappa, theta, sigma, rho, QuadraticExponential) model = HestonModel(process) option = VanillaOption(c_payoff, exercise) engine = AnalyticHestonEngine(model, 144) option.set_pricing_engine(engine) price_fft = option.net_present_value engine = MCEuropeanHestonEngine( process, antithetic_variate=True, steps_per_year=nb_steps_a, required_samples=nb_paths, seed=seed) option.set_pricing_engine(engine) price_mc = option.net_present_value expected = 15.1796 tolerance = .05 self.assertAlmostEqual(price_fft, expected, delta=tolerance) self.assertAlmostEqual(price_mc, expected, delta=tolerance)
def test_black_calibration(self): # calibrate a Heston model to a constant volatility surface without # smile. expected result is a vanishing volatility of the volatility. # In addition theta and v0 should be equal to the constant variance todays_date = today() self.settings.evaluation_date = todays_date daycounter = Actual360() calendar = NullCalendar() risk_free_ts = flat_rate(0.04, daycounter) dividend_ts = flat_rate(0.50, daycounter) option_maturities = [ Period(1, Months), Period(2, Months), Period(3, Months), Period(6, Months), Period(9, Months), Period(1, Years), Period(2, Years) ] options = [] s0 = SimpleQuote(1.0) vol = SimpleQuote(0.1) volatility = vol.value for maturity in option_maturities: for moneyness in np.arange(-1.0, 2.0, 1.): tau = daycounter.year_fraction( risk_free_ts.reference_date, calendar.advance( risk_free_ts.reference_date, period=maturity) ) forward_price = s0.value * dividend_ts.discount(tau) / \ risk_free_ts.discount(tau) strike_price = forward_price * np.exp( -moneyness * volatility * np.sqrt(tau) ) options.append( HestonModelHelper( maturity, calendar, s0.value, strike_price, vol, risk_free_ts, dividend_ts ) ) for sigma in np.arange(0.1, 0.7, 0.2): v0 = 0.01 kappa = 0.2 theta = 0.02 rho = -0.75 process = HestonProcess( risk_free_ts, dividend_ts, s0, v0, kappa, theta, sigma, rho ) self.assertEqual(v0, process.v0) self.assertEqual(kappa, process.kappa) self.assertEqual(theta, process.theta) self.assertEqual(sigma, process.sigma) self.assertEqual(rho, process.rho) self.assertEqual(1.0, process.s0.value) model = HestonModel(process) engine = AnalyticHestonEngine(model, 96) for option in options: option.set_pricing_engine(engine) optimisation_method = LevenbergMarquardt(1e-8, 1e-8, 1e-8) end_criteria = EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8) model.calibrate(options, optimisation_method, end_criteria) tolerance = 3.0e-3 self.assertFalse(model.sigma > tolerance) self.assertAlmostEqual( model.kappa * model.theta, model.kappa * volatility ** 2, delta=tolerance ) self.assertAlmostEqual(model.v0, volatility ** 2, delta=tolerance)
def test_zanette(self): """ From paper by A. Zanette et al. """ dc = Actual365Fixed() todays_date = today() settings = Settings() settings.evaluation_date = todays_date # constant yield and div curves dates = [todays_date + Period(i, Years) for i in range(3)] rates = [0.04 for i in range(3)] divRates = [0.03 for i in range(3)] r_ts = ZeroCurve(dates, rates, dc) q_ts = ZeroCurve(dates, divRates, dc) s0 = SimpleQuote(100) # Heston model v0 = .1 kappa_v = 2 theta_v = 0.1 sigma_v = 0.3 rho_sv = -0.5 hestonProcess = HestonProcess(risk_free_rate_ts=r_ts, dividend_ts=q_ts, s0=s0, v0=v0, kappa=kappa_v, theta=theta_v, sigma=sigma_v, rho=rho_sv) hestonModel = HestonModel(hestonProcess) # Hull-White kappa_r = 1 sigma_r = .2 hullWhiteProcess = HullWhiteProcess(r_ts, a=kappa_r, sigma=sigma_r) strike = 100 maturity = 1 type = Call maturity_date = todays_date + Period(maturity, Years) exercise = EuropeanExercise(maturity_date) payoff = PlainVanillaPayoff(type, strike) option = VanillaOption(payoff, exercise) def price_cal(rho, tGrid): fd_hestonHwEngine = FdHestonHullWhiteVanillaEngine( hestonModel, hullWhiteProcess, rho, tGrid, 100, 40, 20, 0, True, FdmSchemeDesc.Hundsdorfer()) option.set_pricing_engine(fd_hestonHwEngine) return option.npv calc_price = [] for rho in [-0.5, 0, .5]: for tGrid in [50, 100, 150, 200]: tmp = price_cal(rho, tGrid) print("rho (S,r): %f Ns: %d Price: %f" % (rho, tGrid, tmp)) calc_price.append(tmp) expected_price = [ 11.38, ] * 4 + [ 12.79, ] * 4 + [ 14.06, ] * 4 np.testing.assert_almost_equal(calc_price, expected_price, 2)
def test_DAX_calibration(self): # this example is taken from A. Sepp # Pricing European-Style Options under Jump Diffusion Processes # with Stochstic Volatility: Applications of Fourier Transform # http://math.ut.ee/~spartak/papers/stochjumpvols.pdf settlement_date = Date(5, July, 2002) self.settings.evaluation_date = settlement_date daycounter = Actual365Fixed() calendar = TARGET() t = [13, 41, 75, 165, 256, 345, 524, 703] r = [0.0357,0.0349,0.0341,0.0355,0.0359,0.0368,0.0386,0.0401] dates = [settlement_date] + [settlement_date + val for val in t] rates = [0.0357] + r risk_free_ts = ZeroCurve(dates, rates, daycounter) dividend_ts = FlatForward( settlement_date, forward=0.0, daycounter=daycounter ) v = [ 0.6625,0.4875,0.4204,0.3667,0.3431,0.3267,0.3121,0.3121, 0.6007,0.4543,0.3967,0.3511,0.3279,0.3154,0.2984,0.2921, 0.5084,0.4221,0.3718,0.3327,0.3155,0.3027,0.2919,0.2889, 0.4541,0.3869,0.3492,0.3149,0.2963,0.2926,0.2819,0.2800, 0.4060,0.3607,0.3330,0.2999,0.2887,0.2811,0.2751,0.2775, 0.3726,0.3396,0.3108,0.2781,0.2788,0.2722,0.2661,0.2686, 0.3550,0.3277,0.3012,0.2781,0.2781,0.2661,0.2661,0.2681, 0.3428,0.3209,0.2958,0.2740,0.2688,0.2627,0.2580,0.2620, 0.3302,0.3062,0.2799,0.2631,0.2573,0.2533,0.2504,0.2544, 0.3343,0.2959,0.2705,0.2540,0.2504,0.2464,0.2448,0.2462, 0.3460,0.2845,0.2624,0.2463,0.2425,0.2385,0.2373,0.2422, 0.3857,0.2860,0.2578,0.2399,0.2357,0.2327,0.2312,0.2351, 0.3976,0.2860,0.2607,0.2356,0.2297,0.2268,0.2241,0.2320 ] s0 = SimpleQuote(4468.17) strikes = [ 3400, 3600, 3800, 4000, 4200, 4400, 4500, 4600, 4800, 5000, 5200, 5400, 5600 ] options = [] for s, strike in enumerate(strikes): for m in range(len(t)): vol = SimpleQuote(v[s * 8 + m]) # round to weeks maturity = Period((int)((t[m] + 3) / 7.), Weeks) options.append( HestonModelHelper( maturity, calendar, s0.value, strike, vol, risk_free_ts, dividend_ts, ImpliedVolError ) ) v0 = 0.1 kappa = 1.0 theta = 0.1 sigma = 0.5 rho = -0.5 process = HestonProcess( risk_free_ts, dividend_ts, s0, v0, kappa, theta, sigma, rho ) model = HestonModel(process) engine = AnalyticHestonEngine(model, 64) for option in options: option.set_pricing_engine(engine) om = LevenbergMarquardt(1e-8, 1e-8, 1e-8) model.calibrate( options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8) ) sse = 0 for i in range(len(strikes) * len(t)): diff = options[i].calibration_error() * 100.0 sse += diff * diff expected = 177.2 # see article by A. Sepp. self.assertAlmostEqual(expected, sse, delta=1.0)
def test_compare_BsmHW_HestonHW(self): """ From Quantlib test suite """ print("Comparing European option pricing for a BSM " + "process with one-factor Hull-White model...") 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) 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) tol = 1e-5 strikes = [0.25, 0.5, 0.75, 0.8, 0.9, 1.0, 1.1, 1.2, 1.5, 2.0, 4.0] maturities = [1, 2, 3, 5, 10, 15, 20, 25, 30] types = [Put, Call] for option_type in types: for strike in strikes: for maturity in maturities: maturity_date = todays_date + Period(maturity, Years) exercise = EuropeanExercise(maturity_date) fwd = strike * s0.value * \ q_ts.discount(maturity_date) / \ r_ts.discount(maturity_date) payoff = PlainVanillaPayoff(option_type, fwd) option = VanillaOption(payoff, exercise) option.set_pricing_engine(bsmhwEngine) calculated = option.npv option.set_pricing_engine(hestonHwEngine) expected = option.npv if ((np.abs(expected - calculated) > calculated * tol) and (np.abs(expected - calculated) > tol)): print("Failed to reproduce npv") print("strike : %f" % strike) print("maturity : %d" % maturity) print("type : %s" % option_type.name) self.assertAlmostEqual(expected, calculated, 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)
# <markdowncell> # The simulation # -------------- # # The *simulate* function is not part of Quantlib. It has been added to the pyQL interface (see folder quantlib/sim). This illustrates how to crerate extensions to Quantlib and expose them to python. # <codecell> import pylab as pl from quantlib.sim.simulate import simulateHeston # simulate and plot Heston paths paths = 2 steps = 100 horizon = 2 seed = 12345 model = HestonModel(process) res = simulateHeston(model, paths, steps, horizon, seed, antithetic=True) time = res[0, :] simulations = res[1:, :].T pl.plot(time, simulations) pl.xlabel('Time') pl.ylabel('Stock Price') pl.title('Heston Process Simulation') show()
def heston_calibration(df_option, ival=None): """ calibrate heston model """ # extract rates and div yields from the data set df_tmp = DataFrame.filter(df_option, items=['dtExpiry', 'iRate', 'iDiv']) grouped = df_tmp.groupby('dtExpiry') def aggregate(serie): return serie[serie.index[0]] df_rates = grouped.agg(aggregate) # Get first index: first_index = 0 dtTrade = df_option['dtTrade'][first_index] # back out the spot from any forward iRate = df_option['iRate'][first_index] iDiv = df_option['iDiv'][first_index] TTM = df_option['TTM'][first_index] Fwd = df_option['Fwd'][first_index] spot = SimpleQuote(Fwd*np.exp(-(iRate-iDiv)*TTM)) print('Spot: %f risk-free rate: %f div. yield: %f' % (spot.value, iRate, iDiv)) # build array of option helpers hh = heston_helpers(spot, df_option, dtTrade, df_rates) options = hh['options'] spot = hh['spot'] risk_free_ts = dfToZeroCurve(df_rates['iRate'], dtTrade) dividend_ts = dfToZeroCurve(df_rates['iDiv'], dtTrade) # initial values for parameters if ival is None: ival = {'v0': 0.1, 'kappa': 1.0, 'theta': 0.1, 'sigma': 0.5, 'rho': -.5} process = HestonProcess( risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'], ival['theta'], ival['sigma'], ival['rho']) model = HestonModel(process) engine = AnalyticHestonEngine(model, 64) for option in options: option.set_pricing_engine(engine) om = LevenbergMarquardt(1e-8, 1e-8, 1e-8) model.calibrate( options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8) ) print('model calibration results:') print('v0: %f kappa: %f theta: %f sigma: %f rho: %f' % (model.v0, model.kappa, model.theta, model.sigma, model.rho)) calib_error = (1.0/len(options)) * sum( [pow(o.calibration_error()*100.0,2) for o in options]) print('SSE: %f' % calib_error) # merge the fitted volatility and the input data set return merge_df(df_option, options, 'Heston')
def test_smith(self): # test against result published in # Journal of Computational Finance Vol. 11/1 Fall 2007 # An almost exact simulation method for the heston model def payoff(o, scenario): Strike = o['S'] if o['CP'] == 'C': exercise = [max(ST - Strike, 0) for ST in scenario] else: exercise = [max(-ST + Strike, 0) for ST in scenario] return np.mean(exercise) settlement_date = today() self.settings.evaluation_date = settlement_date daycounter = ActualActual() timeToMaturity = 4 exercise_date = settlement_date + timeToMaturity * 365 c_payoff = PlainVanillaPayoff(Call, 100) exercise = EuropeanExercise(exercise_date) risk_free_ts = flat_rate(0., daycounter) dividend_ts = flat_rate(0., daycounter) s0 = SimpleQuote(100.0) v0 = 0.0194 kappa = 1.0407 theta = 0.0586 sigma = 0.5196 rho = -.6747 nb_steps_a = 100 nb_paths = 20000 seed = 12347 process = HestonProcess(risk_free_ts, dividend_ts, s0, v0, kappa, theta, sigma, rho, QUADRATICEXPONENTIAL) model = HestonModel(process) option = VanillaOption(c_payoff, exercise) engine = AnalyticHestonEngine(model, 144) option.set_pricing_engine(engine) price_fft = option.net_present_value engine = MCVanillaEngine(trait='MCEuropeanHestonEngine', RNG='PseudoRandom', process=process, doAntitheticVariate=True, stepsPerYear=nb_steps_a, requiredSamples=nb_paths, seed=seed) option.set_pricing_engine(engine) price_mc = option.net_present_value expected = 15.1796 tolerance = .05 self.assertAlmostEqual(price_fft, expected, delta=tolerance) self.assertAlmostEqual(price_mc, expected, delta=tolerance)