def test_recursion(self):
"""Test recursion."""
nobs = 10
ndim = 3
persistence = .99
beta = .85
volmean = .2
acorr = .05
bcorr = .9
rho = .5
param = np.array([acorr, bcorr, -.01])
const = np.eye(ndim) * (1 - acorr - bcorr)
data, rho_series = DCC.simulate(nobs=nobs,
ndim=ndim,
volmean=volmean,
persistence=persistence,
beta=beta,
acorr=acorr,
bcorr=bcorr,
rho=rho)
data = data.values
neg_data = data.copy()
neg_data[neg_data > 0] = 0
qmat1 = np.zeros((nobs, ndim, ndim))
qmat2 = np.zeros((nobs, ndim, ndim))
qmat1[0] = const.copy()
qmat2[0] = const.copy()
corr_dcc1 = np.zeros((nobs, ndim, ndim))
corr_dcc2 = np.zeros((nobs, ndim, ndim))
dcc_recursion_python(qmat1, const, data, neg_data, param)
dcc_recursion_numba(qmat2, const, data, neg_data, param)
npt.assert_array_almost_equal(qmat1, qmat2)
corr_dcc_python(corr_dcc1, qmat1)
corr_dcc_numba(corr_dcc2, qmat2)
npt.assert_array_almost_equal(corr_dcc1, corr_dcc2)
def test_recursion(self):
"""Test recursion."""
nobs = 10
ndim = 3
persistence = .99
beta = .85
volmean = .2
acorr = .05
bcorr = .9
rho = .5
param = np.array([acorr, bcorr, -.01])
const = np.eye(ndim) * (1 - acorr - bcorr)
data, rho_series = DCC.simulate(nobs=nobs, ndim=ndim, volmean=volmean,
persistence=persistence, beta=beta,
acorr=acorr, bcorr=bcorr, rho=rho)
data = data.values
neg_data = data.copy()
neg_data[neg_data > 0] = 0
qmat1 = np.zeros((nobs, ndim, ndim))
qmat2 = np.zeros((nobs, ndim, ndim))
qmat1[0] = const.copy()
qmat2[0] = const.copy()
corr_dcc1 = np.zeros((nobs, ndim, ndim))
corr_dcc2 = np.zeros((nobs, ndim, ndim))
dcc_recursion_python(qmat1, const, data, neg_data, param)
dcc_recursion_numba(qmat2, const, data, neg_data, param)
npt.assert_array_almost_equal(qmat1, qmat2)
corr_dcc_python(corr_dcc1, qmat1)
corr_dcc_numba(corr_dcc2, qmat2)
npt.assert_array_almost_equal(corr_dcc1, corr_dcc2)
sns.set()
nobs = 500
ndim = 3
persistence = .99
beta = .85
volmean = .2
acorr = .05
bcorr = .9
rho = .5
ret, rho_series = DCC.simulate(nobs=nobs,
ndim=ndim,
volmean=volmean,
persistence=persistence,
beta=beta,
acorr=acorr,
bcorr=bcorr,
rho=rho)
model = DCC(ret=ret)
with take_time('Python'):
result = model.fit(method='Nelder-Mead', numba=False)
with take_time('Numba'):
result = model.fit(method='Nelder-Mead', numba=True)
print(model.data)
model.data.plot_returns()
model.data.plot_std_returns()
pd.set_option('float_format', '{:6.4f}'.format)
np.set_printoptions(precision=4, suppress=True)
sns.set()
nobs = 500
ndim = 3
persistence = .99
beta = .85
volmean = .2
acorr = .05
bcorr = .9
rho = .5
ret, rho_series = DCC.simulate(nobs=nobs, ndim=ndim, volmean=volmean,
persistence=persistence, beta=beta,
acorr=acorr, bcorr=bcorr, rho=rho)
model = DCC(ret=ret)
with take_time('Python'):
result = model.fit(method='Nelder-Mead', numba=False)
with take_time('Numba'):
result = model.fit(method='Nelder-Mead', numba=True)
print(model.data)
model.data.plot_returns()
model.data.plot_std_returns()
model.data.plot_innov()