def test_egarch(self):
nobs = self.nobs
parameters = np.array([0.0, 0.1, -0.1, 0.95])
resids, sigma2 = self.resids, self.sigma2
p = o = q = 1
backcast = 0.0
var_bounds = self.var_bounds
lnsigma2 = np.empty_like(sigma2)
std_resids = np.empty_like(sigma2)
abs_std_resids = np.empty_like(sigma2)
recpy.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs,
backcast, var_bounds, lnsigma2, std_resids,
abs_std_resids)
sigma2_numba = sigma2.copy()
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
sigma2_python = sigma2.copy()
rec.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs,
backcast, var_bounds, lnsigma2, std_resids,
abs_std_resids)
assert_almost_equal(sigma2_numba, sigma2)
assert_almost_equal(sigma2_python, sigma2)
norm_const = np.sqrt(2 / np.pi)
for t in range(nobs):
lnsigma2[t] = parameters[0]
if t == 0:
lnsigma2[t] += parameters[3] * backcast
else:
stdresid = resids[t - 1] / np.sqrt(sigma2[t - 1])
lnsigma2[t] += parameters[1] * (np.abs(stdresid) - norm_const)
lnsigma2[t] += parameters[2] * stdresid
lnsigma2[t] += parameters[3] * lnsigma2[t - 1]
sigma2[t] = np.exp(lnsigma2[t])
assert_almost_equal(sigma2_python, sigma2)
parameters = np.array([-100.0, 0.1, -0.1, 0.95])
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([0.0, 0.1, -0.1, 9.5])
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([0.0, 0.1, -0.1, 0.95])
mod_resids = resids.copy()
mod_resids[:1] = np.inf
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
def test_egarch(self):
nobs = self.nobs
parameters = np.array([0.0, 0.1, -0.1, 0.95])
resids, sigma2 = self.resids, self.sigma2
p = o = q = 1
backcast = 0.0
var_bounds = self.var_bounds
lnsigma2 = np.empty_like(sigma2)
std_resids = np.empty_like(sigma2)
abs_std_resids = np.empty_like(sigma2)
recpy.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs,
backcast, var_bounds, lnsigma2, std_resids,
abs_std_resids)
sigma2_numba = sigma2.copy()
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
sigma2_python = sigma2.copy()
rec.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs,
backcast, var_bounds, lnsigma2, std_resids,
abs_std_resids)
assert_almost_equal(sigma2_numba, sigma2)
assert_almost_equal(sigma2_python, sigma2)
norm_const = np.sqrt(2 / np.pi)
for t in range(nobs):
lnsigma2[t] = parameters[0]
if t == 0:
lnsigma2[t] += parameters[3] * backcast
else:
stdresid = resids[t - 1] / np.sqrt(sigma2[t - 1])
lnsigma2[t] += parameters[1] * (np.abs(stdresid) - norm_const)
lnsigma2[t] += parameters[2] * stdresid
lnsigma2[t] += parameters[3] * lnsigma2[t - 1]
sigma2[t] = np.exp(lnsigma2[t])
assert_almost_equal(sigma2_python, sigma2)
parameters = np.array([-100.0, 0.1, -0.1, 0.95])
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([0.0, 0.1, -0.1, 9.5])
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
parameters = np.array([0.0, 0.1, -0.1, 0.95])
mod_resids = resids.copy()
mod_resids[:1] = np.inf
recpy.egarch_recursion_python(parameters, resids, sigma2, p, o, q,
nobs, backcast, var_bounds, lnsigma2,
std_resids, abs_std_resids)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 2 * self.var_bounds[:, 1])
def test_egarch_performance(self):
egarch_setup = """
parameters = np.array([0.0, 0.1, -0.1, 0.95])
p = o = q = 1
backcast = 0.0
lnsigma2 = np.empty_like(sigma2)
std_resids = np.empty_like(sigma2)
abs_std_resids = np.empty_like(sigma2)
"""
egarch_first = """
recpy.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs, backcast,
var_bounds, lnsigma2, std_resids, abs_std_resids)
"""
egarch_second = """
rec.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs, backcast,
var_bounds, lnsigma2, std_resids, abs_std_resids)
"""
timer = Timer(egarch_first, 'Numba', egarch_second, 'Cython', 'EGARCH',
self.timer_setup + egarch_setup)
timer.display()
assert timer.ratio < 10.0
if not (missing_numba or CYTHON_COVERAGE):
assert 0.1 < timer.ratio
def test_egarch_performance(self):
egarch_setup = """
parameters = np.array([0.0, 0.1, -0.1, 0.95])
p = o = q = 1
backcast = 0.0
lnsigma2 = np.empty_like(sigma2)
std_resids = np.empty_like(sigma2)
abs_std_resids = np.empty_like(sigma2)
"""
egarch_first = """
recpy.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs, backcast,
var_bounds, lnsigma2, std_resids, abs_std_resids)
"""
egarch_second = """
rec.egarch_recursion(parameters, resids, sigma2, p, o, q, nobs, backcast,
var_bounds, lnsigma2, std_resids, abs_std_resids)
"""
timer = Timer(egarch_first, 'Numba', egarch_second, 'Cython', 'EGARCH',
self.timer_setup + egarch_setup)
timer.display()
assert timer.ratio < 10.0
if not (missing_numba or CYTHON_COVERAGE):
assert 0.1 < timer.ratio
def test_egarch(self):
nobs = self.nobs
parameters = np.array([0.0, 0.1, -0.1, 0.95])
resids, sigma2 = self.resids, self.sigma2
p = o = q = 1
backcast = 0.0
var_bounds = self.var_bounds
lnsigma2 = np.empty_like(sigma2)
std_resids = np.empty_like(sigma2)
abs_std_resids = np.empty_like(sigma2)
recpy.egarch_recursion(
parameters,
resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
sigma2_numba = sigma2.copy()
recpy.egarch_recursion_python(
parameters,
resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
sigma2_python = sigma2.copy()
rec.egarch_recursion(
parameters,
resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
assert_almost_equal(sigma2_numba, sigma2)
assert_almost_equal(sigma2_python, sigma2)
sigma2[:] = np.nan
eu = recpy.EGARCHUpdater(p, o, q)
eu.initialize_update(parameters, backcast, nobs)
for t in range(nobs):
eu._update_tester(t, parameters, resids, sigma2, self.var_bounds)
if t == nobs // 2:
eu = pickle.loads(pickle.dumps(eu))
eu.initialize_update(parameters, backcast, nobs)
assert_allclose(sigma2, sigma2_python)
sigma2[:] = np.nan
eu = rec.EGARCHUpdater(p, o, q)
eu.initialize_update(parameters, backcast, nobs)
for t in range(nobs):
eu._update_tester(t, parameters, resids, sigma2, self.var_bounds)
if t == nobs // 2:
eu = pickle.loads(pickle.dumps(eu))
eu.initialize_update(parameters, backcast, nobs)
assert_allclose(sigma2, sigma2_python)
norm_const = np.sqrt(2 / np.pi)
for t in range(nobs):
lnsigma2[t] = parameters[0]
if t == 0:
lnsigma2[t] += parameters[3] * backcast
else:
stdresid = resids[t - 1] / np.sqrt(sigma2[t - 1])
lnsigma2[t] += parameters[1] * (np.abs(stdresid) - norm_const)
lnsigma2[t] += parameters[2] * stdresid
lnsigma2[t] += parameters[3] * lnsigma2[t - 1]
sigma2[t] = np.exp(lnsigma2[t])
assert_almost_equal(sigma2_python, sigma2)
parameters = np.array([-100.0, 0.1, -0.1, 0.95])
recpy.egarch_recursion_python(
parameters,
resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 1.1 * self.var_bounds[:, 1])
parameters = np.array([0.0, 0.1, -0.1, 9.5])
recpy.egarch_recursion_python(
parameters,
resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 1.1 * self.var_bounds[:, 1])
parameters = np.array([0.0, 0.1, -0.1, 0.95])
mod_resids = resids.copy()
mod_resids[:1] = np.inf
recpy.egarch_recursion_python(
parameters,
resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 1.1 * self.var_bounds[:, 1])
parameters = np.array([0.0, 31, -0.1, 0.95])
mod_resids = resids.copy()
mod_resids[100] = np.finfo(float).max
recpy.egarch_recursion_python(
parameters,
mod_resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 1.1 * self.var_bounds[:, 1])
rec.egarch_recursion(
parameters,
mod_resids,
sigma2,
p,
o,
q,
nobs,
backcast,
var_bounds,
lnsigma2,
std_resids,
abs_std_resids,
)
assert np.all(sigma2 >= self.var_bounds[:, 0])
assert np.all(sigma2 <= 1.1 * self.var_bounds[:, 1])
