def test_model_names(self):
garch = GARCH(2, 0, 0)
assert_equal(garch.name, 'ARCH')
garch = GARCH(2, 0, 2)
assert_equal(garch.name, 'GARCH')
garch = GARCH(2, 2, 2)
assert_equal(garch.name, 'GJR-GARCH')
garch = GARCH(1, 0, 0, power=1.0)
assert_equal(garch.name, 'AVARCH')
garch = GARCH(1, 0, 1, power=1.0)
assert_equal(garch.name, 'AVGARCH')
garch = GARCH(1, 1, 1, power=1.0)
assert_equal(garch.name, 'TARCH/ZARCH')
garch = GARCH(3, 0, 0, power=1.5)
assert_equal(garch.name, 'Power ARCH (power: 1.5)')
assert 'Power' in garch.__str__()
garch = GARCH(1, 2, 1, power=1.5)
assert_equal(garch.name, 'Asym. Power GARCH (power: 1.5)')
garch = GARCH(2, 0, 2, power=1.5)
assert_equal(garch.name, 'Power GARCH (power: 1.5)')
def test_model_names(self):
garch = GARCH(2, 0, 0)
assert_equal(garch.name, 'ARCH')
garch = GARCH(2, 0, 2)
assert_equal(garch.name, 'GARCH')
garch = GARCH(2, 2, 2)
assert_equal(garch.name, 'GJR-GARCH')
garch = GARCH(1, 0, 0, power=1.0)
assert_equal(garch.name, 'AVARCH')
garch = GARCH(1, 0, 1, power=1.0)
assert_equal(garch.name, 'AVGARCH')
garch = GARCH(1, 1, 1, power=1.0)
assert_equal(garch.name, 'TARCH/ZARCH')
garch = GARCH(3, 0, 0, power=1.5)
assert_equal(garch.name, 'Power ARCH (power: 1.5)')
assert_true('Power' in garch.__str__())
garch = GARCH(1, 2, 1, power=1.5)
assert_equal(garch.name, 'Asym. Power GARCH (power: 1.5)')
garch = GARCH(2, 0, 2, power=1.5)
assert_equal(garch.name, 'Power GARCH (power: 1.5)')
def test_garch(self):
garch = GARCH()
sv = garch.starting_values(self.resids)
assert_equal(sv.shape[0], garch.num_params)
bounds = garch.bounds(self.resids)
assert_equal(bounds[0], (0.0, 10.0 * np.mean(self.resids ** 2.0)))
assert_equal(bounds[1], (0.0, 1.0))
assert_equal(bounds[2], (0.0, 1.0))
backcast = garch.backcast(self.resids)
w = 0.94 ** np.arange(75)
assert_almost_equal(backcast,
np.sum((self.resids[:75] ** 2) * (w / w.sum())))
var_bounds = garch.variance_bounds(self.resids)
parameters = np.array([.1, .1, .8])
garch.compute_variance(parameters, self.resids, self.sigma2,
backcast, var_bounds)
cond_var_direct = np.zeros_like(self.sigma2)
rec.garch_recursion(parameters,
self.resids ** 2.0,
np.sign(self.resids),
cond_var_direct,
1, 0, 1, self.T, backcast, var_bounds)
assert_allclose(self.sigma2, cond_var_direct)
a, b = garch.constraints()
a_target = np.vstack((np.eye(3), np.array([[0, -1.0, -1.0]])))
b_target = np.array([0.0, 0.0, 0.0, -1.0])
assert_array_equal(a, a_target)
assert_array_equal(b, b_target)
state = np.random.get_state()
rng = Normal()
sim_data = garch.simulate(parameters, self.T, rng.simulate([]))
np.random.set_state(state)
e = np.random.standard_normal(self.T + 500)
initial_value = 1.0
sigma2 = np.zeros(self.T + 500)
data = np.zeros(self.T + 500)
for t in range(self.T + 500):
sigma2[t] = parameters[0]
shock = initial_value if t == 0 else data[t - 1] ** 2.0
sigma2[t] += parameters[1] * shock
lagged_value = initial_value if t == 0 else sigma2[t - 1]
sigma2[t] += parameters[2] * lagged_value
data[t] = e[t] * np.sqrt(sigma2[t])
data = data[500:]
sigma2 = sigma2[500:]
assert_almost_equal(data / sim_data[0], np.ones_like(data))
assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2))
names = garch.parameter_names()
names_target = ['omega', 'alpha[1]', 'beta[1]']
assert_equal(names, names_target)
assert isinstance(garch.__str__(), str)
txt = garch.__repr__()
assert str(hex(id(garch))) in txt
assert_equal(garch.name, 'GARCH')
assert_equal(garch.num_params, 3)
assert_equal(garch.power, 2.0)
assert_equal(garch.p, 1)
assert_equal(garch.o, 0)
assert_equal(garch.q, 1)
def test_garch(self):
garch = GARCH()
sv = garch.starting_values(self.resids)
assert_equal(sv.shape[0], garch.num_params)
bounds = garch.bounds(self.resids)
assert_equal(bounds[0], (0.0, 10.0 * np.mean(self.resids ** 2.0)))
assert_equal(bounds[1], (0.0, 1.0))
assert_equal(bounds[2], (0.0, 1.0))
backcast = garch.backcast(self.resids)
w = 0.94 ** np.arange(75)
assert_almost_equal(backcast,
np.sum((self.resids[:75] ** 2) * (w / w.sum())))
var_bounds = garch.variance_bounds(self.resids)
parameters = np.array([.1, .1, .8])
garch.compute_variance(parameters, self.resids, self.sigma2,
backcast, var_bounds)
cond_var_direct = np.zeros_like(self.sigma2)
rec.garch_recursion(parameters,
self.resids ** 2.0,
np.sign(self.resids),
cond_var_direct,
1, 0, 1, self.T, backcast, var_bounds)
assert_allclose(self.sigma2, cond_var_direct)
A, b = garch.constraints()
A_target = np.vstack((np.eye(3), np.array([[0, -1.0, -1.0]])))
b_target = np.array([0.0, 0.0, 0.0, -1.0])
assert_array_equal(A, A_target)
assert_array_equal(b, b_target)
state = np.random.get_state()
rng = Normal()
sim_data = garch.simulate(parameters, self.T, rng.simulate([]))
np.random.set_state(state)
e = np.random.standard_normal(self.T + 500)
initial_value = 1.0
sigma2 = np.zeros(self.T + 500)
data = np.zeros(self.T + 500)
for t in range(self.T + 500):
sigma2[t] = parameters[0]
shock = initial_value if t == 0 else data[t - 1] ** 2.0
sigma2[t] += parameters[1] * shock
lagged_value = initial_value if t == 0 else sigma2[t - 1]
sigma2[t] += parameters[2] * lagged_value
data[t] = e[t] * np.sqrt(sigma2[t])
data = data[500:]
sigma2 = sigma2[500:]
assert_almost_equal(data / sim_data[0], np.ones_like(data))
assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2))
names = garch.parameter_names()
names_target = ['omega', 'alpha[1]', 'beta[1]']
assert_equal(names, names_target)
assert_true(isinstance(garch.__str__(), str))
repr = garch.__repr__()
assert_true(str(hex(id(garch))) in repr)
assert_equal(garch.name, 'GARCH')
assert_equal(garch.num_params, 3)
assert_equal(garch.power, 2.0)
assert_equal(garch.p, 1)
assert_equal(garch.o, 0)
assert_equal(garch.q, 1)
