def test_binomial_variable_hazard(self):
"""Test variable hazard rate."""
dS = WienerJumpProcess(0.1, 0.2, lambda S: (S / 100)**-1, cap_lambda=True)
S = np.linspace(0, 200, 401)
for pu, pd, po in zip(*dS.binomial(1, S)):
self.assertGreaterEqual(min(pu, pd, po), 0)
self.assertEqual(pu + pd + po, 1)
dS.cap_lambda = False
self.assertRaises(ValueError, dS.binomial, 1, S)
def test_binomial(self):
"""Test parameters for the binomial model."""
dS = WienerJumpProcess(0.1, 0.2, 0.1, 0.3)
u, d, l, (pu, pd, po) = dS.binomial(1)
self.assertGreater(u, np.exp(0.1))
self.assertLess(d, np.exp(0.1))
self.assertGreater(d, 0)
self.assertGreaterEqual(l, 0)
self.assertLessEqual(l, 1)
self.assertGreaterEqual(min(pu, pd, po), 0)
self.assertEqual(pu + pd + po, 1)
dS.r = 1
self.assertRaises(ValueError, dS.binomial, 0.2)
dS.lambd_ = np.double('inf')
self.assertRaises(ValueError, dS.binomial, 0.01)
self.assertRaises(ValueError, dS.binomial, 0)
self.assertRaises(ValueError, dS.binomial, -0.01)
