def batesdoubleexp_calibration(df_option,
dtTrade=None,
df_rates=None,
ival=None):
# array of option helpers
hh = heston_helpers(df_option, dtTrade, df_rates, ival)
options = hh['options']
spot = hh['spot']
risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)
v0 = .02
if ival is None:
ival = {
'v0': v0,
'kappa': 3.7,
'theta': v0,
'sigma': 1.0,
'rho': -.6,
'lambda': .1,
'nu': -.5,
'delta': 0.3
}
process = HestonProcess(risk_free_ts, dividend_ts, spot, ival['v0'],
ival['kappa'], ival['theta'], ival['sigma'],
ival['rho'])
model = BatesDoubleExpModel(process)
engine = BatesDoubleExpEngine(model, 64)
for option in options:
option.set_pricing_engine(engine)
om = LevenbergMarquardt()
model.calibrate(options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8))
print('BatesDoubleExpModel calibration:')
print(
'v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: %f \
nuUp: %f nuDown: %f\np: %f' %
(model.v0, model.kappa, model.theta, model.sigma, model.rho,
model.Lambda, model.nuUp, model.nuDown, model.p))
calib_error = (1.0 / len(options)) * sum(
[pow(o.calibration_error(), 2) for o in options])
print('SSE: %f' % calib_error)
return merge_df(df_option, options, 'BatesDoubleExp')
def batesdoubleexp_calibration(df_option, dtTrade=None,
df_rates=None, ival=None):
# array of option helpers
hh = heston_helpers(df_option, dtTrade, df_rates, ival)
options = hh['options']
spot = hh['spot']
risk_free_ts = df_to_zero_curve(df_rates['R'], dtTrade)
dividend_ts = df_to_zero_curve(df_rates['D'], dtTrade)
v0 = .02
if ival is None:
ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
'sigma': 1.0, 'rho': -.6, 'lambda': .1,
'nu': -.5, 'delta': 0.3}
process = HestonProcess(
risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
ival['theta'], ival['sigma'], ival['rho'])
model = BatesDoubleExpModel(process)
engine = BatesDoubleExpEngine(model, 64)
for option in options:
option.set_pricing_engine(engine)
om = LevenbergMarquardt()
model.calibrate(
options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
)
print('BatesDoubleExpModel calibration:')
print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: %f \
nuUp: %f nuDown: %f\np: %f' %
(model.v0, model.kappa, model.theta, model.sigma,
model.rho, model.Lambda, model.nuUp, model.nuDown,
model.p))
calib_error = (1.0 / len(options)) * sum(
[pow(o.calibration_error(), 2) for o in options])
print('SSE: %f' % calib_error)
return merge_df(df_option, options, 'BatesDoubleExp')
def test_heston_process(self):
ph = HestonProcess(self.risk_free_ts, self.dividend_ts, self.s0,
self.v0, self.kappa, self.theta, self.sigma,
self.rho)
self.assertIsNotNone(ph)
# constructor with default arguments
me = BatesDoubleExpModel(ph)
self.assertIsNotNone(me)
# specify the arguments
me = BatesDoubleExpModel(ph,
Lambda=0.234,
nuUp=0.43,
nuDown=0.54,
p=.6)
self.assertIsNotNone(me)
def test_simulate_batesDoubleExpModel(self):
model = BatesDoubleExpModel(self.heston_process)
paths = 4
steps = 10
horizon = 1
seed = 12345
tolerance = 1.e-3
res = simulateBatesDoubleExpModel(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)