def blsprice(spot, strike, risk_free_rate, time, volatility,
option_type='Call', dividend=0.0):
"""
Black-Scholes option pricing model
"""
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 _blsimpv(price, spot, strike, risk_free_rate, time,
option_type='Call', dividend=0.0):
spot = SimpleQuote(spot)
daycounter = ActualActual()
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 _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 test_settings_instance_method(self):
Settings.instance().evaluation_date = today()
self.assertEqual(
today(),
Settings.instance().evaluation_date
)
def test_settings_instance_method(self):
Settings.instance().evaluation_date = today()
self.assertEqual(
today(),
Settings.instance().evaluation_date
)
def zero_curve(ts):
days = range(10, 365*20, 30)
dtMat = [calendar.advance(today(), d, Days) for d in days]
df = np.array([ts.discount(dt) for dt in dtMat])
dtMat = [QLDateTodate(dt) for dt in dtMat]
dtToday = QLDateTodate(today())
dt = np.array([(d-dtToday).days/365.0 for d in dtMat])
zc = -np.log(df) / dt
return (dtMat, zc)
def zero_curve(ts):
days = range(10, 365 * 20, 30)
dtMat = [calendar.advance(today(), d, Days) for d in days]
df = np.array([ts.discount(dt) for dt in dtMat])
dtMat = [QLDateTodate(dt) for dt in dtMat]
dtToday = QLDateTodate(today())
dt = np.array([(d - dtToday).days / 365.0 for d in dtMat])
zc = -np.log(df) / dt
return (dtMat, zc)
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 setUp(self):
settlement_date = today()
settings = Settings()
settings.evaluation_date = settlement_date
daycounter = ActualActual()
self.calendar = NullCalendar()
i_rate = .1
i_div = .04
self.risk_free_ts = flat_rate(i_rate, daycounter)
self.dividend_ts = flat_rate(i_div, daycounter)
self.s0 = SimpleQuote(32.0)
# Bates model
self.v0 = 0.05
self.kappa = 5.0
self.theta = 0.05
self.sigma = 1.0e-4
self.rho = 0.0
self.Lambda = .1
self.nu = .01
self.delta = .001
def test_create_libor_index(self):
settings = Settings.instance()
# Market information
calendar = UnitedStates(LiborImpact)
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
term_structure = YieldTermStructure(relinkable=True)
term_structure.link_to(FlatForward(settlement_date, 0.05,
Actual365Fixed()))
index = Libor('USDLibor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360(),
term_structure)
default_libor = USDLibor(Period(6, Months))
for attribute in ["business_day_convention", "end_of_month",
"fixing_calendar", "joint_calendar", "tenor",
"fixing_days", "day_counter", "family_name", "name"]:
self.assertEqual(getattr(index, attribute),
getattr(default_libor, attribute))
def setUp(self):
atm_option_tenors = [Period(1, Months), Period(6, Months)] + \
[Period(i, Years) for i in [1, 5, 10, 30]]
atm_swap_tenors = [Period(1, Years), Period(5, Years), Period(10, Years),
Period(30, Years)]
m = np.array([[.1300, .1560, .1390, .1220],
[.1440, .1580, .1460, .1260],
[.1600, .1590, .1470, .1290],
[.1640, .1470, .1370, .1220],
[.1400, .1300, .1250, .1100],
[.1130, .1090, .1070, .0930]])
M = Matrix.from_ndarray(m)
calendar = UnitedStates()
self.atm_vol = SwaptionVolatilityMatrix(calendar,
Following,
atm_option_tenors,
atm_swap_tenors,
M,
Actual365Fixed())
reference_date = calendar.adjust(today())
Settings().evaluation_date = reference_date
self.term_structure = FlatForward(reference_date, 0.05, Actual365Fixed())
self.swap_index = EuriborSwapIsdaFixA(Period(10, Years),
forwarding=self.term_structure)
def test_create_libor_index(self):
settings = Settings.instance()
# Market information
calendar = UnitedStates(LiborImpact)
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
term_structure = YieldTermStructure(relinkable=True)
term_structure.link_to(
FlatForward(settlement_date, 0.05, Actual365Fixed()))
index = Libor('USDLibor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360(), term_structure)
default_libor = USDLibor(Period(6, Months))
for attribute in [
"business_day_convention", "end_of_month", "fixing_calendar",
"joint_calendar", "tenor", "fixing_days", "day_counter",
"family_name", "name"
]:
self.assertEqual(getattr(index, attribute),
getattr(default_libor, attribute))
def test_blsprice(self):
from quantlib.settings import Settings
from quantlib.time.api import today
Settings.instance().evaluation_date = today()
call_value = blsprice(100.0, 97.0, 0.1, 0.25, 0.5)
self.assertAlmostEquals(call_value, 12.61, 2)
def test_create_swap_index(self):
settings = Settings.instance()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date);
ibor_index = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360())
index = SwapIndex(
'family name', Period(3, Months), 10, USDCurrency(), TARGET(),
Period(12, Months), Following, Actual360(), ibor_index)
self.assertIsNotNone(index)
def test_create_libor_index(self):
settings = Settings.instance()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
term_structure = YieldTermStructure(relinkable=True)
term_structure.link_to(
FlatForward(settlement_date, 0.05, Actual365Fixed()))
index = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360(), term_structure)
t = index.tenor
self.assertEquals(t.length, 6)
self.assertEquals(t.units, 2)
self.assertEquals('USD Libor6M Actual/360', index.name)
def setUp(self):
""""""
self.today = today()
self.settings = Settings()
self.settings.evaluation_date = self.today
self.dc = Actual365Fixed()
self.spot = SimpleQuote(0.0)
self.q_rate = SimpleQuote(0.0)
self.q_ts = FlatForward(self.today, self.q_rate, self.dc)
self.r_rate = SimpleQuote(0.0)
self.r_ts = FlatForward(self.today, self.r_rate, self.dc)
self.values = {
'type': SwapType.Long,
'strike': 0.04,
'nominal': 50000,
's': 100.0,
'q': 0.00,
'r': 0.05,
't': 0.246575,
'v': 0.20,
'result': 0.04189,
'tol': 1.0e-4,
}
self.spot.value = self.values['s']
self.q_rate.value = self.values['q']
self.r_rate.value = self.values['r']
self.ex_date = self.today + int(self.values['t'] * 365 + 0.5)
def test_create_swap_index(self):
settings = Settings.instance()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
term_structure = YieldTermStructure(relinkable=True)
term_structure.link_to(FlatForward(settlement_date, 0.05,
Actual365Fixed()))
ibor_index = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360(),
term_structure)
index = SwapIndex(
'family name', Period(3, Months), 10, USDCurrency(), TARGET(),
Period(12, Months), Following, Actual360(), ibor_index)
self.assertIsNotNone(index)
def setUp(self):
settlement_date = today()
settings = Settings()
settings.evaluation_date = settlement_date
daycounter = ActualActual()
self.calendar = NullCalendar()
i_rate = .1
i_div = .04
self.risk_free_ts = flat_rate(i_rate, daycounter)
self.dividend_ts = flat_rate(i_div, daycounter)
self.s0 = SimpleQuote(32.0)
# Bates model
self.v0 = 0.05
self.kappa = 5.0
self.theta = 0.05
self.sigma = 1.0e-4
self.rho = 0.0
self.Lambda = .1
self.nu = .01
self.delta = .001
def test_create_swap_index(self):
settings = Settings.instance()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
term_structure = YieldTermStructure(relinkable=True)
term_structure.link_to(
FlatForward(settlement_date, 0.05, Actual365Fixed()))
ibor_index = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360(),
term_structure)
index = SwapIndex('family name', Period(3, Months), 10, USDCurrency(),
TARGET(), Period(12, Months), Following, Actual360(),
ibor_index)
self.assertIsNotNone(index)
def test_create_libor_index(self):
settings = Settings.instance()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
term_structure = YieldTermStructure(relinkable=True)
term_structure.link_to(FlatForward(settlement_date, 0.05,
Actual365Fixed()))
index = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360(),
term_structure)
t = index.tenor
self.assertEqual(t.length, 6)
self.assertEqual(t.units, 2)
self.assertEqual('USD Libor6M Actual/360', index.name)
def test_blsprice(self):
from quantlib.settings import Settings
from quantlib.time.api import today
Settings.instance().evaluation_date = today()
call_value = blsprice(100.0, 97.0, 0.1, 0.25, 0.5)
self.assertAlmostEquals(call_value, 12.61, 2)
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_creation(self):
settings = Settings()
# Market information
calendar = TARGET()
# must be a business day
settings.evaluation_date = calendar.adjust(today())
settlement_date = Date(18, September, 2008)
# must be a business day
settlement_date = calendar.adjust(settlement_date);
quotes = [SimpleQuote(0.0096), SimpleQuote(0.0145), SimpleQuote(0.0194)]
tenors = [3, 6, 12]
rate_helpers = []
calendar = TARGET()
deposit_day_counter = Actual365Fixed()
convention = ModifiedFollowing
end_of_month = True
for quote, month in zip(quotes, tenors):
tenor = Period(month, Months)
fixing_days = 3
helper = DepositRateHelper(
quote, tenor, fixing_days, calendar, convention, end_of_month,
deposit_day_counter
)
rate_helpers.append(helper)
ts_day_counter = ActualActual(ISDA)
tolerance = 1.0e-15
ts = PiecewiseYieldCurve[BootstrapTrait.Discount, LogLinear].from_reference_date(
settlement_date, rate_helpers,
ts_day_counter, accuracy=tolerance
)
self.assertIsNotNone(ts)
self.assertEqual( Date(18, September, 2008), ts.reference_date)
# this is not a real test ...
self.assertAlmostEqual(0.9975, ts.discount(Date(21, 12, 2008)), 4)
self.assertAlmostEqual(0.9944, ts.discount(Date(21, 4, 2009)), 4)
self.assertAlmostEqual(0.9904, ts.discount(Date(21, 9, 2009)), 4)
dates, dfs = zip(*ts.nodes)
self.assertAlmostEqual(list(dates), ts.dates)
self.assertAlmostEqual(list(dfs), ts.data)
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 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 test_creation(self):
settings = Settings()
# Market information
calendar = TARGET()
# must be a business day
settings.evaluation_date = calendar.adjust(today())
settlement_date = Date(18, September, 2008)
# must be a business day
settlement_date = calendar.adjust(settlement_date);
quotes = [0.0096, 0.0145, 0.0194]
tenors = [3, 6, 12]
rate_helpers = []
calendar = TARGET()
deposit_day_counter = Actual365Fixed()
convention = ModifiedFollowing
end_of_month = True
for quote, month in zip(quotes, tenors):
tenor = Period(month, Months)
fixing_days = 3
helper = DepositRateHelper(
quote, tenor, fixing_days, calendar, convention, end_of_month,
deposit_day_counter
)
rate_helpers.append(helper)
ts_day_counter = ActualActual(ISDA)
tolerance = 1.0e-15
ts = term_structure_factory(
'discount', 'loglinear', settlement_date, rate_helpers,
ts_day_counter, tolerance
)
self.assertIsNotNone(ts)
self.assertEquals( Date(18, September, 2008), ts.reference_date)
# this is not a real test ...
self.assertAlmostEquals(0.9975, ts.discount(Date(21, 12, 2008)), 4)
self.assertAlmostEquals(0.9944, ts.discount(Date(21, 4, 2009)), 4)
self.assertAlmostEquals(0.9904, ts.discount(Date(21, 9, 2009)), 4)
def test_creation(self):
settings = Settings()
# Market information
calendar = TARGET()
# must be a business day
settings.evaluation_date = calendar.adjust(today())
settlement_date = Date(18, September, 2008)
# must be a business day
settlement_date = calendar.adjust(settlement_date);
quotes = [SimpleQuote(0.0096), SimpleQuote(0.0145), SimpleQuote(0.0194)]
tenors = [3, 6, 12]
rate_helpers = []
calendar = TARGET()
deposit_day_counter = Actual365Fixed()
convention = ModifiedFollowing
end_of_month = True
for quote, month in zip(quotes, tenors):
tenor = Period(month, Months)
fixing_days = 3
helper = DepositRateHelper(
quote, tenor, fixing_days, calendar, convention, end_of_month,
deposit_day_counter
)
rate_helpers.append(helper)
ts_day_counter = ActualActual(ISDA)
tolerance = 1.0e-15
ts = PiecewiseYieldCurve.from_reference_date(
BootstrapTrait.Discount, Interpolator.LogLinear, settlement_date, rate_helpers,
ts_day_counter, tolerance
)
self.assertIsNotNone(ts)
self.assertEqual( Date(18, September, 2008), ts.reference_date)
# this is not a real test ...
self.assertAlmostEqual(0.9975, ts.discount(Date(21, 12, 2008)), 4)
self.assertAlmostEqual(0.9944, ts.discount(Date(21, 4, 2009)), 4)
self.assertAlmostEqual(0.9904, ts.discount(Date(21, 9, 2009)), 4)
def test_creation(self):
settings = Settings()
# Market information
calendar = TARGET()
# must be a business day
settings.evaluation_date = calendar.adjust(today())
settlement_date = Date(18, September, 2008)
# must be a business day
settlement_date = calendar.adjust(settlement_date);
quotes = [0.0096, 0.0145, 0.0194]
tenors = [3, 6, 12]
rate_helpers = []
calendar = TARGET()
deposit_day_counter = Actual365Fixed()
convention = ModifiedFollowing
end_of_month = True
for quote, month in zip(quotes, tenors):
tenor = Period(month, Months)
fixing_days = 3
helper = DepositRateHelper(
quote, tenor, fixing_days, calendar, convention, end_of_month,
deposit_day_counter
)
rate_helpers.append(helper)
ts_day_counter = ActualActual(ISDA)
tolerance = 1.0e-15
ts = term_structure_factory(
'discount', 'loglinear', settlement_date, rate_helpers,
ts_day_counter, tolerance
)
self.assertIsNotNone(ts)
self.assertEquals( Date(18, September, 2008), ts.reference_date)
# this is not a real test ...
self.assertAlmostEquals(0.9975, ts.discount(Date(21, 12, 2008)), 4)
self.assertAlmostEquals(0.9944, ts.discount(Date(21, 4, 2009)), 4)
self.assertAlmostEquals(0.9904, ts.discount(Date(21, 9, 2009)), 4)
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 test_using_settings(self):
settings = Settings()
evaluation_date = today()
# have to set the evaluation date before the test as it is a global
# attribute for the whole library ... meaning that previous test_cases
# might have set this to another date
settings.evaluation_date = evaluation_date
self.assertEqual(settings.evaluation_date, evaluation_date)
self.assertTrue(settings.version.startswith('1'))
def test_using_settings(self):
settings = Settings()
evaluation_date = today()
# have to set the evaluation date before the test as it is a global
# attribute for the whole library ... meaning that previous test_cases
# might have set this to another date
settings.evaluation_date = evaluation_date
self.assertEqual(settings.evaluation_date, evaluation_date)
self.assertTrue(settings.version.startswith('1'))
def test_bond_schedule_today(self):
'''Test date calculations and role of settings when evaluation date
set to current date.
'''
todays_date = today()
settings = Settings()
settings.evaluation_date = todays_date
calendar = TARGET()
effective_date = Date(10, Jul, 2006)
termination_date = calendar.advance(
effective_date, 10, Years, convention=Unadjusted)
settlement_days = 3
face_amount = 100.0
coupon_rate = 0.05
redemption = 100.0
fixed_bond_schedule = Schedule.from_rule(
effective_date,
termination_date,
Period(Annual),
calendar,
ModifiedFollowing,
ModifiedFollowing,
Rule.Backward
)
issue_date = effective_date
bond = FixedRateBond(
settlement_days,
face_amount,
fixed_bond_schedule,
[coupon_rate],
ActualActual(ISMA),
Following,
redemption,
issue_date
)
self.assertEqual(
calendar.advance(todays_date, 3, Days), bond.settlement_date())
def test_bond_schedule_today(self):
'''Test date calculations and role of settings when evaluation date
set to current date.
'''
todays_date = today()
settings = Settings()
settings.evaluation_date = todays_date
calendar = TARGET()
effective_date = Date(10, Jul, 2006)
termination_date = calendar.advance(
effective_date, 10, Years, convention=Unadjusted)
settlement_days = 3
face_amount = 100.0
coupon_rate = 0.05
redemption = 100.0
fixed_bond_schedule = Schedule.from_rule(
effective_date,
termination_date,
Period(Annual),
calendar,
ModifiedFollowing,
ModifiedFollowing,
Rule.Backward
)
issue_date = effective_date
bond = FixedRateBond(
settlement_days,
face_amount,
fixed_bond_schedule,
[coupon_rate],
ActualActual(ISMA),
Following,
redemption,
issue_date
)
self.assertEqual(
calendar.advance(todays_date, 3, Days), bond.settlement_date())
def setUp(self):
calendar = TARGET()
today_date = today()
Settings().evaluation_date = today_date
hazard_rate = SimpleQuote(0.01234)
probability_curve = FlatHazardRate(0, calendar, hazard_rate, Actual360())
discount_curve = FlatForward(today_date, 0.06, Actual360())
issue_date = today_date
#calendar.advance(today_date, -1, Years)
maturity = calendar.advance(issue_date, 10, Years)
self.convention = Following
self.schedule = Schedule(issue_date, maturity, Period("3M"), calendar,
self.convention, self.convention, Rule.TwentiethIMM)
recovery_rate = 0.4
self.engine = MidPointCdsEngine(probability_curve, recovery_rate, discount_curve, True)
def test_simulate_heston_1(self):
settings = self.settings
settlement_date = today()
settings.evaluation_date = settlement_date
# simulate Heston paths
paths = 4
steps = 10
horizon = 1
seed = 12345
grid = TimeGrid(horizon, steps)
res = simulate_process(self.heston_process, paths, grid, seed)
time = list(grid)
time_expected = np.arange(0, 1.1, .1)
np.testing.assert_array_almost_equal(time, time_expected, decimal=4)
def test_create_libor_index(self):
settings = Settings.instance()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
index = Libor("USD Libor", Period(6, Months), settlement_days, USDCurrency(), calendar, Actual360())
self.assertEquals("USD Libor6M Actual/360", index.name)
def test_simulate_heston_1(self):
settings = self.settings
settlement_date = today()
settings.evaluation_date = settlement_date
# simulate Heston paths
paths = 4
steps = 10
horizon = 1
seed = 12345
grid = TimeGrid(horizon, steps)
res = simulate_process(self.heston_process, paths, grid, seed)
time = list(grid)
time_expected = np.arange(0, 1.1, .1)
np.testing.assert_array_almost_equal(time, time_expected, decimal=4)
def test_create_libor_index(self):
settings = Settings.instance()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date)
index = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360())
self.assertEquals('USD Libor6M Actual/360', index.name)
def test_simulate_heston_1(self):
settings = self.settings
settlement_date = today()
settings.evaluation_date = settlement_date
# simulate Heston paths
paths = 4
steps = 10
horizon = 1
seed = 12345
model = HestonModel(self.heston_process)
res = simulate_model(model, paths, steps, horizon, seed)
time = res[0, :]
time_expected = np.arange(0, 1.1, .1)
simulations = res[1:, :].T
np.testing.assert_array_almost_equal(time, time_expected, decimal=4)
def test_simulate_heston_1(self):
settings = self.settings
settlement_date = today()
settings.evaluation_date = settlement_date
# simulate Heston paths
paths = 4
steps = 10
horizon = 1
seed = 12345
model = HestonModel(self.heston_process)
res = simulateHeston(model, paths, steps, horizon, seed)
time = res[0, :]
time_expected = np.arange(0, 1.1, 0.1)
simulations = res[1:, :].T
np.testing.assert_array_almost_equal(time, time_expected, decimal=4)
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)
# The Heston Process
# ------------------
# <codecell>
def flat_rate(forward, daycounter):
return FlatForward(
quote = SimpleQuote(forward),
settlement_days = 0,
calendar = NullCalendar(),
daycounter = daycounter
)
settings = Settings.instance()
settlement_date = today()
settings.evaluation_date = settlement_date
daycounter = ActualActual()
calendar = NullCalendar()
interest_rate = .1
dividend_yield = .04
risk_free_ts = flat_rate(interest_rate, daycounter)
dividend_ts = flat_rate(dividend_yield, daycounter)
s0 = SimpleQuote(100.0)
# Heston model
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_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_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)
from quantlib.pricingengines.vanilla import AnalyticEuropeanEngine
from quantlib.processes.black_scholes_process import BlackScholesMertonProcess
from quantlib.quotes import SimpleQuote
from quantlib.settings import Settings
from quantlib.time.api import TARGET, Actual365Fixed, today, Date as QlDate
from quantlib.termstructures.yields.api import FlatForward
from quantlib.termstructures.volatility.equityfx.black_vol_term_structure \
import BlackConstantVol
settings = Settings.instance()
calendar = TARGET()
offset = 366
todays_date = today() - offset
settlement_date = todays_date + 2
settings.evaluation_date = todays_date
class OptionValuation(HasTraits):
# options parameters
option_type = Enum(Put, Call)
underlying = Float(36)
strike = Float(40)
dividend_yield = Range(0.0, 0.5)
risk_free_rate = Range(0.0, 0.2)
volatility = Range(0.0, 0.5)
maturity = Date(datetime.date.today())
def test_bates_det_jump(self):
# this looks like a bug in QL:
# Bates Det Jump model does not have sigma as parameter, yet
# changing sigma changes the result!
settlement_date = today()
self.settings.evaluation_date = settlement_date
daycounter = ActualActual()
exercise_date = settlement_date + 6 * Months
payoff = PlainVanillaPayoff(Put, 1290)
exercise = EuropeanExercise(exercise_date)
option = VanillaOption(payoff, exercise)
risk_free_ts = flat_rate(0.02, daycounter)
dividend_ts = flat_rate(0.04, daycounter)
spot = 1290
ival = {'delta': 3.6828677022272715e-06,
'kappa': 19.02581428347027,
'kappaLambda': 1.1209758060939223,
'lambda': 0.06524550732595163,
'nu': -1.8968106563601956,
'rho': -0.7480898462264719,
'sigma': 1.0206363887835108,
'theta': 0.01965384459461113,
'thetaLambda': 0.028915397380738218,
'v0': 0.06566800935242285}
process = BatesProcess(
risk_free_ts, dividend_ts, SimpleQuote(spot),
ival['v0'], ival['kappa'],
ival['theta'], ival['sigma'], ival['rho'],
ival['lambda'], ival['nu'], ival['delta'])
model = BatesDetJumpModel(process,
ival['kappaLambda'], ival['thetaLambda'])
engine = BatesDetJumpEngine(model, 64)
option.set_pricing_engine(engine)
calc_1 = option.net_present_value
ival['sigma'] = 1.e-6
process = BatesProcess(
risk_free_ts, dividend_ts, SimpleQuote(spot),
ival['v0'], ival['kappa'],
ival['theta'], ival['sigma'], ival['rho'],
ival['lambda'], ival['nu'], ival['delta'])
model = BatesDetJumpModel(process,
ival['kappaLambda'], ival['thetaLambda'])
engine = BatesDetJumpEngine(model, 64)
option.set_pricing_engine(engine)
calc_2 = option.net_present_value
if(abs(calc_1-calc_2) > 1.e-5):
print('calc 1 %f calc 2 %f' % (calc_1, calc_2))
self.assertNotEqual(calc_1, calc_2)
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 = MCVanillaEngine(
trait='MCEuropeanHestonEngine',
generator='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)
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_bates_det_jump(self):
# this looks like a bug in QL:
# Bates Det Jump model does not have sigma as parameter, yet
# changing sigma changes the result!
settlement_date = today()
self.settings.evaluation_date = settlement_date
daycounter = ActualActual()
exercise_date = settlement_date + 6 * Months
payoff = PlainVanillaPayoff(Put, 1290)
exercise = EuropeanExercise(exercise_date)
option = VanillaOption(payoff, exercise)
risk_free_ts = flat_rate(0.02, daycounter)
dividend_ts = flat_rate(0.04, daycounter)
spot = 1290
ival = {'delta': 3.6828677022272715e-06,
'kappa': 19.02581428347027,
'kappaLambda': 1.1209758060939223,
'lambda': 0.06524550732595163,
'nu': -1.8968106563601956,
'rho': -0.7480898462264719,
'sigma': 1.0206363887835108,
'theta': 0.01965384459461113,
'thetaLambda': 0.028915397380738218,
'v0': 0.06566800935242285}
process = BatesProcess(
risk_free_ts, dividend_ts, SimpleQuote(spot),
ival['v0'], ival['kappa'],
ival['theta'], ival['sigma'], ival['rho'],
ival['lambda'], ival['nu'], ival['delta'])
model = BatesDetJumpModel(process,
ival['kappaLambda'], ival['thetaLambda'])
engine = BatesDetJumpEngine(model, 64)
option.set_pricing_engine(engine)
calc_1 = option.net_present_value
ival['sigma'] = 1.e-6
process = BatesProcess(
risk_free_ts, dividend_ts, SimpleQuote(spot),
ival['v0'], ival['kappa'],
ival['theta'], ival['sigma'], ival['rho'],
ival['lambda'], ival['nu'], ival['delta'])
model = BatesDetJumpModel(process,
ival['kappaLambda'], ival['thetaLambda'])
engine = BatesDetJumpEngine(model, 64)
option.set_pricing_engine(engine)
calc_2 = option.net_present_value
if(abs(calc_1-calc_2) > 1.e-5):
print('calc 1 %f calc 2 %f' % (calc_1, calc_2))
self.assertNotEqual(calc_1, calc_2)
def test_deposit_swap(self):
settings = Settings()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date);
depositData = [[ 1, Months, 4.581 ],
[ 2, Months, 4.573 ],
[ 3, Months, 4.557 ],
[ 6, Months, 4.496 ],
[ 9, Months, 4.490 ]]
swapData = [[ 1, Years, 4.54 ],
[ 5, Years, 4.99 ],
[ 10, Years, 5.47 ],
[ 20, Years, 5.89 ],
[ 30, Years, 5.96 ]]
rate_helpers = []
end_of_month = True
for m, period, rate in depositData:
tenor = Period(m, Months)
helper = DepositRateHelper(rate/100, tenor, settlement_days,
calendar, ModifiedFollowing, end_of_month,
Actual360())
rate_helpers.append(helper)
liborIndex = Libor('USD Libor', Period(6, Months), settlement_days,
USDCurrency(), calendar, Actual360())
spread = SimpleQuote(0)
fwdStart = Period(0, Days)
for m, period, rate in swapData:
rate = SimpleQuote(rate/100)
helper = SwapRateHelper(rate, Period(m, Years),
calendar, Annual,
Unadjusted, Thirty360(),
liborIndex, spread, fwdStart)
rate_helpers.append(helper)
ts_day_counter = ActualActual(ISDA)
tolerance = 1.0e-15
ts = term_structure_factory(
'discount', 'loglinear', settlement_date, rate_helpers,
ts_day_counter, tolerance)
# this is not a real test ...
self.assertAlmostEquals(0.9103,
ts.discount(calendar.advance(today(), 2, Years)),3)
self.assertAlmostEquals(0.7836,
ts.discount(calendar.advance(today(), 5, Years)),3)
self.assertAlmostEquals(0.5827,
ts.discount(calendar.advance(today(), 10, Years)),3)
self.assertAlmostEquals(0.4223,
ts.discount(calendar.advance(today(), 15, Years)),3)
def get_term_structure(df_libor, dtObs):
settings = Settings()
# Market information
calendar = TARGET()
# must be a business day
eval_date = calendar.adjust(today())
settings.evaluation_date = eval_date
settlement_days = 2
settlement_date = calendar.advance(eval_date, settlement_days, Days)
# must be a business day
settlement_date = calendar.adjust(settlement_date);
depositData =[[1, Months, 'Libor1M'],
[3, Months, 'Libor3M'],
[6, Months, 'Libor6M']]
swapData = [[ 1, Years, 'Swap1Y'],
[ 2, Years, 'Swap2Y'],
[ 3, Years, 'Swap3Y'],
[ 4, Years, 'Swap4Y'],
[ 5, Years, 'Swap5Y'],
[ 7, Years, 'Swap7Y'],
[ 10, Years,'Swap10Y'],
[ 30, Years,'Swap30Y']]
rate_helpers = []
end_of_month = True
for m, period, label in depositData:
tenor = Period(m, Months)
rate = df_libor.get_value(dtObs, label)
helper = DepositRateHelper(float(rate/100), tenor,
settlement_days,
calendar, ModifiedFollowing, end_of_month,
Actual360())
rate_helpers.append(helper)
endOfMonth = True
liborIndex = Libor('USD Libor', Period(6, Months),
settlement_days,
USDCurrency(), calendar,
ModifiedFollowing,
endOfMonth, Actual360())
spread = SimpleQuote(0)
fwdStart = Period(0, Days)
for m, period, label in swapData:
rate = df_libor.get_value(dtObs, label)
helper = SwapRateHelper(SimpleQuote(rate/100),
Period(m, Years),
calendar, Annual,
Unadjusted, Thirty360(),
liborIndex, spread, fwdStart)
rate_helpers.append(helper)
ts_day_counter = ActualActual(ISDA)
tolerance = 1.0e-15
ts = term_structure_factory(
'discount', 'loglinear', settlement_date, rate_helpers,
ts_day_counter, tolerance)
return ts
def test_zero_curve_on_swap_index(self):
todays_date = today()
calendar = UnitedStates() # INPUT
dayCounter = Actual360() # INPUT
currency = USDCurrency() # INPUT
Settings.instance().evaluation_date = todays_date
settlement_days = 2
settlement_date = calendar.advance(
todays_date, period=Period(settlement_days, Days)
)
liborRates = [ SimpleQuote(0.002763), SimpleQuote(0.004082), SimpleQuote(0.005601), SimpleQuote(0.006390), SimpleQuote(0.007125),
SimpleQuote(0.007928), SimpleQuote(0.009446), SimpleQuote(0.01110)]
liborRatesTenor = [Period(tenor, Months) for tenor in [1,2,3,4,5,6,9,12]]
Libor_dayCounter = Actual360();
swapRates = [SimpleQuote(0.005681), SimpleQuote(0.006970), SimpleQuote(0.009310), SimpleQuote(0.012010), SimpleQuote(0.014628),
SimpleQuote(0.016881), SimpleQuote(0.018745), SimpleQuote(0.020260), SimpleQuote(0.021545)]
swapRatesTenor = [Period(i, Years) for i in range(2, 11)]
# 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()
)
SwapFamilyName = currency.name + "swapIndex"
instruments = []
# ++++++++++++++++++++ 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))
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
tolerance = 1.0e-15
ts = PiecewiseYieldCurve.from_reference_date(
BootstrapTrait.ZeroYield, Interpolator.Linear, settlement_date, instruments, dayCounter,
tolerance
)
self.assertEqual(settlement_date, ts.reference_date)
# The Heston Process
# ------------------
# <codecell>
def flat_rate(forward, daycounter):
return FlatForward(
forward=SimpleQuote(forward),
settlement_days=0,
calendar=NullCalendar(),
daycounter=daycounter
)
settings = Settings.instance()
settlement_date = today()
settings.evaluation_date = settlement_date
daycounter = ActualActual()
calendar = NullCalendar()
interest_rate = .1
dividend_yield = .04
risk_free_ts = flat_rate(interest_rate, daycounter)
dividend_ts = flat_rate(dividend_yield, daycounter)
s0 = SimpleQuote(100.0)
# Heston model
def test_zero_curve_on_swap_index(self):
todays_date = today()
calendar = UnitedStates() # INPUT
dayCounter = Actual360() # INPUT
currency = USDCurrency() # INPUT
Settings.instance().evaluation_date = todays_date
settlement_days = 2
settlement_date = calendar.advance(
todays_date, period=Period(settlement_days, Days)
)
liborRates = [ SimpleQuote(0.002763), SimpleQuote(0.004082), SimpleQuote(0.005601), SimpleQuote(0.006390), SimpleQuote(0.007125),
SimpleQuote(0.007928), SimpleQuote(0.009446), SimpleQuote(0.01110)]
liborRatesTenor = [Period(tenor, Months) for tenor in [1,2,3,4,5,6,9,12]]
Libor_dayCounter = Actual360();
swapRates = [SimpleQuote(0.005681), SimpleQuote(0.006970), SimpleQuote(0.009310), SimpleQuote(0.012010), SimpleQuote(0.014628),
SimpleQuote(0.016881), SimpleQuote(0.018745), SimpleQuote(0.020260), SimpleQuote(0.021545)]
swapRatesTenor = [Period(i, Years) for i in range(2, 11)]
# 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()
)
SwapFamilyName = currency.name + "swapIndex"
instruments = []
# ++++++++++++++++++++ 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))
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
tolerance = 1.0e-15
ts = PiecewiseYieldCurve(
'zero', 'linear', settlement_date, instruments, dayCounter,
tolerance
)
self.assertEqual(settlement_date, ts.reference_date)
