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_analytic_versus_black(self):
settlement_date = today()
self.settings.evaluation_date = settlement_date
daycounter = ActualActual()
exercise_date = settlement_date + 6 * Months
payoff = PlainVanillaPayoff(Put, 30)
exercise = EuropeanExercise(exercise_date)
risk_free_ts = flat_rate(0.1, daycounter)
dividend_ts = flat_rate(0.04, daycounter)
s0 = SimpleQuote(32.0)
v0 = 0.05
kappa = 5.0
theta = 0.05
sigma = 1.0e-4
rho = 0.0
process = HestonProcess(
risk_free_ts, dividend_ts, s0, v0, kappa, theta, sigma, rho
)
option = VanillaOption(payoff, exercise)
engine = AnalyticHestonEngine(HestonModel(process), 144)
option.set_pricing_engine(engine)
calculated = option.net_present_value
year_fraction = daycounter.year_fraction(
settlement_date, exercise_date
)
forward_price = 32 * np.exp((0.1 - 0.04) * year_fraction)
expected = blackFormula(
payoff.type, payoff.strike, forward_price,
np.sqrt(0.05 * year_fraction)
) * np.exp(-0.1 * year_fraction)
tolerance = 2.0e-7
self.assertAlmostEqual(
calculated,
expected,
delta=tolerance
)
def heston_pricer(trade_date, options, params, rates, spot):
"""
Price a list of European options with heston model.
"""
spot = SimpleQuote(spot)
risk_free_ts = df_to_zero_curve(rates[nm.INTEREST_RATE], trade_date)
dividend_ts = df_to_zero_curve(rates[nm.DIVIDEND_YIELD], trade_date)
process = HestonProcess(risk_free_ts, dividend_ts, spot, **params)
model = HestonModel(process)
engine = AnalyticHestonEngine(model, 64)
settlement_date = pydate_to_qldate(trade_date)
settings = Settings()
settings.evaluation_date = settlement_date
modeled_values = np.zeros(len(options))
for index, row in options.T.iteritems():
expiry_date = row[nm.EXPIRY_DATE]
strike = row[nm.STRIKE]
option_type = Call if row[nm.OPTION_TYPE] == nm.CALL_OPTION else Put
payoff = PlainVanillaPayoff(option_type, strike)
expiry_qldate = pydate_to_qldate(expiry_date)
exercise = EuropeanExercise(expiry_qldate)
option = VanillaOption(payoff, exercise)
option.set_pricing_engine(engine)
modeled_values[index] = option.net_present_value
prices = options.filter(
items=[nm.EXPIRY_DATE, nm.STRIKE, nm.OPTION_TYPE, nm.SPOT])
prices[nm.PRICE] = modeled_values
prices[nm.TRADE_DATE] = trade_date
return prices
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_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)
