def test_vargamma(self):
"""Test VarGamma model."""
price, strike = 100, 90
riskfree, maturity = 0, 30 / 365
moneyness = np.log(strike / price) - riskfree * maturity
nu = .2
theta = -.14
sigma = .25
param = VarGammaParam(theta=theta, nu=nu, sigma=sigma)
model = VarGamma(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, (1, ))
moneyness = np.linspace(-.1, .1, 10)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
riskfree = np.zeros_like(moneyness)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
def test_heston(self):
"""Test Heston model."""
price, strike = 100, 90
riskfree, maturity = 0, 30 / 365
moneyness = np.log(strike / price) - riskfree * maturity
lm = 1.5768
mu = .12**2
eta = .5751
rho = -.0
sigma = .12**2
param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)
model = Heston(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, (1, ))
moneyness = np.linspace(-.1, .1, 10)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
riskfree = np.zeros_like(moneyness)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
def test_heston(self):
"""Test Heston model."""
price, strike = 100, 90
riskfree, maturity = 0, 30/365
moneyness = np.log(strike/price) - riskfree * maturity
lm = 1.5768
mu = .12**2
eta = .5751
rho = -.0
sigma = .12**2
param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)
model = Heston(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, (1,))
moneyness = np.linspace(-.1, .1, 10)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
riskfree = np.zeros_like(moneyness)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
def test_argamma(self):
"""Test ARG model."""
price, strike = 100, 90
riskfree, maturity = 0, 30/365
moneyness = np.log(strike/price) - riskfree * maturity
rho = .55
delta = .75
mu = .2**2/365
sigma = .2**2/365
phi = -.0
theta1 = -16.0
theta2 = 20.95
param = ARGParam(rho=rho, delta=delta, mu=mu, sigma=sigma,
phi=phi, theta1=theta1, theta2=theta2)
model = ARG(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, (1,))
moneyness = np.linspace(-.1, .1, 10)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
riskfree = np.zeros_like(moneyness)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
def test_vargamma(self):
"""Test VarGamma model."""
price, strike = 100, 90
riskfree, maturity = 0, 30/365
moneyness = np.log(strike/price) - riskfree * maturity
nu = .2
theta = -.14
sigma = .25
param = VarGammaParam(theta=theta, nu=nu, sigma=sigma)
model = VarGamma(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, (1,))
moneyness = np.linspace(-.1, .1, 10)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
riskfree = np.zeros_like(moneyness)
premium = cosmethod(model, moneyness=moneyness, call=True)
self.assertEqual(premium.shape, moneyness.shape)
def single_premium():
"""Test COS method for floats.
"""
price, strike = 100, 90
riskfree, maturity = 0, 30 / 365
sigma = .15
moneyness = np.log(strike / price) - riskfree * maturity
model = GBM(GBMParam(sigma=sigma), riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
nu = .2
theta = -.14
sigma = .25
param = VarGammaParam(theta=theta, nu=nu, sigma=sigma)
model = VarGamma(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
lm = 1.5768
mu = .12**2
eta = .5751
rho = -.0
sigma = .12**2
param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)
model = Heston(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
rho = .55
delta = .75
mu = .2**2 / 365
sigma = .2**2 / 365
phi = -.0
theta1 = -16.0
theta2 = 20.95
param = ARGParam(rho=rho,
delta=delta,
mu=mu,
sigma=sigma,
phi=phi,
theta1=theta1,
theta2=theta2)
model = ARG(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
def multiple_premia_argamma():
"""Plot model-implied out-of-the-money premium for ARG model.
"""
nobs = 200
moneyness = np.linspace(-.2, .2, nobs)
riskfree, maturity = .1, 30 / 365
call = np.ones_like(moneyness).astype(bool)
call[moneyness < 0] = False
current_vol = .2**2 / 365
rho = .9
delta = 1.1
phi = -.5
price_vol = -1
price_ret = .6
param = ARGParam(rho=rho,
delta=delta,
mu=current_vol,
sigma=current_vol,
phi=phi,
theta1=price_vol,
theta2=price_ret)
model = ARG(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
plt.plot(moneyness, premium)
plt.show()
def test_gbm(self):
"""Test GBM model."""
price, strike = 100, 110
riskfree, maturity = .01, 30/365
call = True
put = np.logical_not(call)
moneyness = lfmoneyness(price, strike, riskfree, maturity)
sigma = .15
model = GBM(GBMParam(sigma=sigma), riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
premium_true = blackscholes_norm(moneyness, maturity, sigma, call)
impvol_model = impvol_bisection(moneyness, maturity, premium, call)
self.assertEqual(premium.shape, (1,))
np.testing.assert_array_almost_equal(premium, premium_true, 3)
np.testing.assert_array_almost_equal(impvol_model, sigma, 2)
moneyness = np.linspace(0, .1, 10)
premium = cosmethod(model, moneyness=moneyness, call=call)
premium_true = blackscholes_norm(moneyness, maturity, sigma, call)
impvol_model = impvol_bisection(moneyness, maturity, premium, call)
impvol_true = np.ones_like(impvol_model) * sigma
self.assertEqual(premium.shape, moneyness.shape)
np.testing.assert_array_almost_equal(premium, premium_true, 2)
np.testing.assert_array_almost_equal(impvol_model, impvol_true, 2)
riskfree = np.zeros_like(moneyness)
premium = cosmethod(model, moneyness=moneyness, call=call)
premium_true = blackscholes_norm(moneyness, maturity, sigma, call)
impvol_model = impvol_bisection(moneyness, maturity, premium, call)
self.assertEqual(premium.shape, moneyness.shape)
np.testing.assert_array_almost_equal(premium, premium_true, 3)
np.testing.assert_array_almost_equal(impvol_model, sigma, 2)
moneyness = np.linspace(-.1, 0, 10)
premium = cosmethod(model, moneyness=moneyness, call=put)
premium_true = blackscholes_norm(moneyness, maturity, sigma, put)
impvol_model = impvol_bisection(moneyness, maturity, premium, put)
np.testing.assert_array_almost_equal(premium, premium_true, 2)
np.testing.assert_array_almost_equal(impvol_model, impvol_true, 2)
def test_gbm(self):
"""Test GBM model."""
price, strike = 100, 110
riskfree, maturity = .01, 30 / 365
call = True
put = np.logical_not(call)
moneyness = lfmoneyness(price, strike, riskfree, maturity)
sigma = .15
model = GBM(GBMParam(sigma=sigma), riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
premium_true = blackscholes_norm(moneyness, maturity, sigma, call)
impvol_model = impvol_bisection(moneyness, maturity, premium, call)
self.assertEqual(premium.shape, (1, ))
np.testing.assert_array_almost_equal(premium, premium_true, 3)
np.testing.assert_array_almost_equal(impvol_model, sigma, 2)
moneyness = np.linspace(0, .1, 10)
premium = cosmethod(model, moneyness=moneyness, call=call)
premium_true = blackscholes_norm(moneyness, maturity, sigma, call)
impvol_model = impvol_bisection(moneyness, maturity, premium, call)
impvol_true = np.ones_like(impvol_model) * sigma
self.assertEqual(premium.shape, moneyness.shape)
np.testing.assert_array_almost_equal(premium, premium_true, 2)
np.testing.assert_array_almost_equal(impvol_model, impvol_true, 2)
riskfree = np.zeros_like(moneyness)
premium = cosmethod(model, moneyness=moneyness, call=call)
premium_true = blackscholes_norm(moneyness, maturity, sigma, call)
impvol_model = impvol_bisection(moneyness, maturity, premium, call)
self.assertEqual(premium.shape, moneyness.shape)
np.testing.assert_array_almost_equal(premium, premium_true, 3)
np.testing.assert_array_almost_equal(impvol_model, sigma, 2)
moneyness = np.linspace(-.1, 0, 10)
premium = cosmethod(model, moneyness=moneyness, call=put)
premium_true = blackscholes_norm(moneyness, maturity, sigma, put)
impvol_model = impvol_bisection(moneyness, maturity, premium, put)
np.testing.assert_array_almost_equal(premium, premium_true, 2)
np.testing.assert_array_almost_equal(impvol_model, impvol_true, 2)
def single_premium():
"""Test COS method for floats.
"""
price, strike = 100, 90
riskfree, maturity = 0, 30/365
sigma = .15
moneyness = np.log(strike/price) - riskfree * maturity
model = GBM(GBMParam(sigma=sigma), riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
nu = .2
theta = -.14
sigma = .25
param = VarGammaParam(theta=theta, nu=nu, sigma=sigma)
model = VarGamma(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
lm = 1.5768
mu = .12**2
eta = .5751
rho = -.0
sigma = .12**2
param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)
model = Heston(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
rho = .55
delta = .75
mu = .2**2/365
sigma = .2**2/365
phi = -.0
theta1 = -16.0
theta2 = 20.95
param = ARGParam(rho=rho, delta=delta, mu=mu, sigma=sigma,
phi=phi, theta1=theta1, theta2=theta2)
model = ARG(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=True)
print(premium)
def multiple_premia_gbm(nobs=2000):
"""Test COS method on the grid.
"""
sigma = .15
price = 1
strike = np.exp(np.linspace(-.1, .1, nobs))
riskfree, maturity = 0, 30/365
moneyness = np.log(strike/price) - riskfree * maturity
call = np.ones_like(moneyness).astype(bool)
call[moneyness < 0] = False
model = GBM(GBMParam(sigma=sigma), riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
plt.plot(strike, premium)
plt.show()
def multiple_premia_gbm(nobs=2000):
"""Test COS method on the grid.
"""
sigma = .15
price = 1
strike = np.exp(np.linspace(-.1, .1, nobs))
riskfree, maturity = 0, 30 / 365
moneyness = np.log(strike / price) - riskfree * maturity
call = np.ones_like(moneyness).astype(bool)
call[moneyness < 0] = False
model = GBM(GBMParam(sigma=sigma), riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
plt.plot(strike, premium)
plt.show()
def option_premium(self, vol=None, moneyness=None, maturity=None,
riskfree=None, call=None, data=None, npoints=2**10):
"""Model implied option premium via COS method.
Parameters
----------
vol : array_like
Current variance per day
moneyness : array_like
Log-forward moneyness, np.log(strike/price) - riskfree * maturity
maturity : float, optional
Maturity of the option or simply time horizon.
Fraction of a year, i.e. 30/365
riskfree : float, optional
Risk-free rate of return per day
call : bool array_like
Call/Put flag
data : pandas DataFrame, record array, or dictionary of arrays
Structured data. Mandatory labels:
vol, moneyness, maturity, riskfree, call
npoints : int
Number of points on the grid. The more the better, but slower.
Returns
-------
array_like
Model implied option premium via COS method
"""
if data is not None:
try:
data = data.to_records()
except:
pass
maturity = data['maturity']
riskfree = data['riskfree']
vol = data['vol']
moneyness = data['moneyness']
call = data['call']
self.maturity = maturity
self.riskfree = riskfree
self.vol = vol
return cosmethod(self, moneyness=moneyness, call=call, npoints=npoints)
def cos_heston_price(S_0, K, r, T, v, v_0, kappa, gamma, rho, call=True):
"""
Pricer for European option (single asset) under Heston dynamic
:param S_0: init stock price
:param K: strike
:param r: risk-free rate
:param T: maturity
:param v: long term mean variance
:param v_0: initial variance
:param kappa: reversion speed
:param gamma: vol coefficient of vol
:param rho: correlation between stock and volatility dynamic
:param call: call if True, put otherwise
:return: Option price
"""
moneyness = np.log(K / S_0) - r * T
param = HestonParam(lm=kappa, mu=v, eta=gamma, rho=rho, sigma=v_0)
model = Heston(param, r, T)
P = cosmethod(model, moneyness=moneyness, call=call)
return P[0]
def multiple_premia_heston(nobs=2000):
"""Test COS method on the grid.
"""
lm = 1.5768
mu = .12**2
eta = .5751
rho = -.0
sigma = .12**2
price = 1
strike = np.exp(np.linspace(-.1, .1, nobs))
maturity = 30/365
riskfree = .01 * np.ones(nobs)
moneyness = np.log(strike/price) - riskfree * maturity
call = np.ones_like(moneyness).astype(bool)
call[moneyness < 0] = False
param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)
model = Heston(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
plt.plot(strike, premium)
plt.show()
def multiple_premia_heston(nobs=2000):
"""Test COS method on the grid.
"""
lm = 1.5768
mu = .12**2
eta = .5751
rho = -.0
sigma = .12**2
price = 1
strike = np.exp(np.linspace(-.1, .1, nobs))
maturity = 30 / 365
riskfree = .01 * np.ones(nobs)
moneyness = np.log(strike / price) - riskfree * maturity
call = np.ones_like(moneyness).astype(bool)
call[moneyness < 0] = False
param = HestonParam(lm=lm, mu=mu, eta=eta, rho=rho, sigma=sigma)
model = Heston(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
plt.plot(strike, premium)
plt.show()
def multiple_premia_argamma():
"""Plot model-implied out-of-the-money premium for ARG model.
"""
nobs = 200
moneyness = np.linspace(-.2, .2, nobs)
riskfree, maturity = .1, 30/365
call = np.ones_like(moneyness).astype(bool)
call[moneyness < 0] = False
current_vol = .2**2/365
rho = .9
delta = 1.1
phi = -.5
price_vol = -1
price_ret = .6
param = ARGParam(rho=rho, delta=delta, mu=current_vol, sigma=current_vol,
phi=phi, theta1=price_vol, theta2=price_ret)
model = ARG(param, riskfree, maturity)
premium = cosmethod(model, moneyness=moneyness, call=call)
plt.plot(moneyness, premium)
plt.show()
