class HestonOption_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonOption(spot=100
, strike=100
, interest_rate=0.03
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 0.4
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/150.0
self.F = HestonFiniteDifferenceEngine(option, nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
# self.F = HestonFiniteDifferenceEngine(H, nspots=100,
# nvols=100, spotdensity=10, varexp=4,
# var_max=12, flip_idx_spot=False,
# flip_idx_var=False, verbose=False,
# force_bandwidth=None,
# force_exact=False)
self.F.init()
self.F.operators[1].diagonalize()
def test_implicit(self):
t, dt = self.F.option.tenor, self.dt
dt = 1.0/600.0
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.F.solve_implicit(t/dt, dt)[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
def test_douglas(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.F.solve_douglas(t/dt, dt)[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
def test_smooth(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.F.solve_smooth(t/dt, dt)[self.F.idx]
ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
npt.assert_allclose(V, ans, rtol=0.001)
class HestonBarrierOption_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonBarrierOption(spot=100
, strike=100
, interest_rate=0.06
, volatility = 0.2
, tenor=1.0
, mean_reversion = 1
, mean_variance = 0.12
, vol_of_variance = 0.3
, correlation = 0.4
, top = (False, 170.0)
)
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0/150.0
self.F = HestonFiniteDifferenceEngine(option, nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
self.F.init()
self.F.operators[1].diagonalize()
self.FG = FDG.HestonFiniteDifferenceEngine(option,
nspots=self.F.grid.shape[0],
nvols=self.F.grid.shape[1])
self.FG.make_operator_templates()
self.FG.set_zero_derivative()
self.FG.scale_and_combine_operators()
def test_implicit(self):
t, dt = self.F.option.tenor, self.dt
dt = 1/600.0
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_implicit(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_implicit(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
npt.assert_array_almost_equal(V, V2, decimal=6)
# npt.assert_allclose(V, ans, rtol=0.001)
def test_douglas(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_douglas(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_douglas(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=6)
def test_hundsdorferverwer(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_hundsdorferverwer(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_hundsdorferverwer(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=7)
def test_smooth(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_smooth(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_smooth(t/dt, dt, self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=7)
class HestonBarrierOption_test(unittest.TestCase):
def setUp(self):
DefaultHeston = HestonBarrierOption(spot=100,
strike=100,
interest_rate=0.06,
volatility=0.2,
tenor=1.0,
mean_reversion=1,
mean_variance=0.12,
vol_of_variance=0.3,
correlation=0.4,
top=(False, 170.0))
option = DefaultHeston
# option = HestonOption(tenor=1, strike=99.0, volatility=0.2,
# mean_reversion=3, mean_variance=0.04,
# vol_of_variance=0.6, correlation=-0.7)
self.dt = 1.0 / 150.0
self.F = HestonFiniteDifferenceEngine(option,
nspots=150,
nvols=80,
force_bandwidth=None,
flip_idx_var=False)
self.F.init()
self.F.operators[1].diagonalize()
self.FG = FDG.HestonFiniteDifferenceEngine(option,
nspots=self.F.grid.shape[0],
nvols=self.F.grid.shape[1])
self.FG.make_operator_templates()
self.FG.set_zero_derivative()
self.FG.scale_and_combine_operators()
def test_implicit(self):
t, dt = self.F.option.tenor, self.dt
dt = 1 / 600.0
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_implicit(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_implicit(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
npt.assert_array_almost_equal(V, V2, decimal=6)
# npt.assert_allclose(V, ans, rtol=0.001)
def test_douglas(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_douglas(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_douglas(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=6)
def test_hundsdorferverwer(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_hundsdorferverwer(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_hundsdorferverwer(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V2, V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=7)
def test_smooth(self):
t, dt = self.F.option.tenor, self.dt
for d, o in self.F.operators.items():
if type(d) != tuple:
assert o.is_tridiagonal(), "%s, %s" % (d, o.D.offsets)
V = self.FG.solve_smooth(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
V2 = self.F.solve_smooth(t / dt, dt,
self.F.grid.domain[-1])[self.F.idx]
# ans = self.F.option.analytical
# print "Spot:", self.F.option.spot
# print "Price:", V, ans, V - ans
# npt.assert_allclose(V, ans, rtol=0.001)
npt.assert_array_almost_equal(V, V2, decimal=7)
