def bates_calibration(df_option, ival=None):
"""
calibrate bates' model
"""
tmp = make_helpers(df_option)
risk_free_ts = tmp['risk_free_rate']
dividend_ts = tmp['dividend_rate']
spot = tmp['spot']
options = tmp['options']
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 = BatesProcess(risk_free_ts, dividend_ts, spot, ival['v0'],
ival['kappa'], ival['theta'], ival['sigma'],
ival['rho'], ival['lambda'], ival['nu'],
ival['delta'])
model = BatesModel(process)
engine = BatesEngine(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('model calibration results:')
print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: \
%f nu: %f delta: %f' % (model.v0, model.kappa, model.theta, model.sigma,
model.rho, model.Lambda, model.nu, model.delta))
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, 'Bates')
def bates_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 = dfToZeroCurve(df_rates['R'], dtTrade)
dividend_ts = dfToZeroCurve(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 = BatesProcess(risk_free_ts, dividend_ts, spot, ival['v0'],
ival['kappa'], ival['theta'], ival['sigma'],
ival['rho'], ival['lambda'], ival['nu'],
ival['delta'])
model = BatesModel(process)
engine = BatesEngine(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('model calibration results:')
print(
'v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: %f nu: %f delta: %f'
% (model.v0, model.kappa, model.theta, model.sigma, model.rho,
model.Lambda, model.nu, model.delta))
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, 'Bates')
def bates_calibration(df_option, ival=None):
"""
calibrate bates' model
"""
tmp = make_helpers(df_option)
risk_free_ts = tmp['risk_free_rate']
dividend_ts = tmp['dividend_rate']
spot = tmp['spot']
options = tmp['options']
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 = BatesProcess(
risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
ival['theta'], ival['sigma'], ival['rho'],
ival['lambda'], ival['nu'], ival['delta'])
model = BatesModel(process)
engine = BatesEngine(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('model calibration results:')
print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: \
%f nu: %f delta: %f' %
(model.v0, model.kappa, model.theta, model.sigma,
model.rho, model.Lambda, model.nu, model.delta))
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, 'Bates')
def bates_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 = dfToZeroCurve(df_rates['R'], dtTrade)
dividend_ts = dfToZeroCurve(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 = BatesProcess(
risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
ival['theta'], ival['sigma'], ival['rho'],
ival['lambda'], ival['nu'], ival['delta'])
model = BatesModel(process)
engine = BatesEngine(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('model calibration results:')
print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: %f nu: %f delta: %f' %
(model.v0, model.kappa, model.theta, model.sigma,
model.rho, model.Lambda, model.nu, model.delta))
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, 'Bates')
def test_batest_process(self):
pb = BatesProcess(self.risk_free_ts, self.dividend_ts, self.s0,
self.v0, self.kappa, self.theta, self.sigma,
self.rho, self.Lambda, self.nu, self.delta)
self.assertIsNotNone(pb)
mb = BatesModel(pb)
self.assertIsNotNone(mb)
def test_simulate_bates(self):
model = BatesModel(self.bates_process)
paths = 4
steps = 10
horizon = 1
seed = 12345
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)
plot(time, simulations)
show()
ival = {
'v0': v0,
'kappa': 3.7,
'theta': v0,
'sigma': 1.0,
'rho': -.6,
'lambda': .1,
'nu': -.5,
'delta': 0.3
}
spot = SimpleQuote(1200)
proc_bates = BatesProcess(risk_free_ts, dividend_ts, spot, ival['v0'],
ival['kappa'], ival['theta'], ival['sigma'],
ival['rho'], ival['lambda'], ival['nu'],
ival['delta'])
model_bates = BatesModel(proc_bates)
res_bates = simulateBates(model_bates, paths, steps, horizon, seed)
time = res_bates[0, :]
simulations = res_bates[1:, :].T
figure()
plot(time, simulations)
show()
