def test_no_param_volatility(self):
cm = ConstantMean(self.y)
cm.volatility = EWMAVariance()
cm.fit(update_freq=0)
cm.volatility = RiskMetrics2006()
cm.fit(update_freq=0)
ar = ARX(self.y, lags=5)
ar.volatility = EWMAVariance()
ar.fit(update_freq=0)
ar.volatility = RiskMetrics2006()
ar.fit(update_freq=0)
def test_no_param_volatility(self):
cm = ConstantMean(self.y)
cm.volatility = EWMAVariance()
cm.fit(update_freq=0, disp=DISPLAY)
cm.volatility = RiskMetrics2006()
cm.fit(update_freq=0, disp=DISPLAY)
ar = ARX(self.y, lags=5)
ar.volatility = EWMAVariance()
ar.fit(update_freq=0, disp=DISPLAY)
ar.volatility = RiskMetrics2006()
ar.fit(update_freq=0, disp=DISPLAY)
assert 'tau0' in str(ar.volatility)
assert 'tau1' in str(ar.volatility)
assert 'kmax' in str(ar.volatility)
def test_rm_2006(self):
rm2006 = RiskMetrics2006()
params = np.empty(0)
resids = self.resids
sigma2 = self.sigma2
nobs = self.nobs
backcast = rm2006.backcast(self.resids)
rm2006.compute_variance(params, resids, sigma2, backcast,
self.var_bounds)
sigma2_ref = sigma2.copy()
sigma2[:] = np.nan
cw = rm2006._ewma_combination_weights()
sp = rm2006._ewma_smoothing_parameters()
ru = recpy.RiskMetrics2006Updater(rm2006.kmax, cw, sp)
ru.initialize_update(None, backcast, None)
for t in range(nobs):
ru._update_tester(t, params, resids, sigma2, self.var_bounds)
if t == nobs // 2:
ru = pickle.loads(pickle.dumps(ru))
ru.initialize_update(None, backcast, None)
assert_allclose(sigma2, sigma2_ref)
sigma2_py = sigma2.copy()
sigma2[:] = np.nan
ru = rec.RiskMetrics2006Updater(rm2006.kmax, cw, sp)
ru.initialize_update(None, backcast, None)
for t in range(nobs):
ru._update_tester(t, params, resids, sigma2, self.var_bounds)
if t == nobs // 2:
ru = pickle.loads(pickle.dumps(ru))
ru.initialize_update(None, backcast, None)
ru.initialize_update(None, 3.0, None)
assert_allclose(sigma2, sigma2_py)
def test_backcast_error(simulated_data):
zm = ZeroMean(simulated_data, volatility=GARCH())
with pytest.raises(ValueError):
zm.fit(backcast=-1, disp=DISPLAY)
zm = ZeroMean(simulated_data, volatility=RiskMetrics2006())
with pytest.raises(ValueError):
zm.fit(backcast=np.ones(100), disp=DISPLAY)
def test_riskmetrics(self):
rm06 = RiskMetrics2006()
sv = rm06.starting_values(self.resids)
assert_equal(sv.shape[0], rm06.num_params)
bounds = rm06.bounds(self.resids)
assert_equal(len(bounds), 0)
var_bounds = rm06.variance_bounds(self.resids)
backcast = rm06.backcast(self.resids)
assert_equal(backcast.shape[0], 14)
parameters = np.array([])
names = rm06.parameter_names()
names_target = []
assert_equal(names, names_target)
# TODO: Test variance fit by RM06
rm06.compute_variance(parameters, self.resids, self.sigma2, backcast,
var_bounds)
A, b = rm06.constraints()
A_target = np.empty((0, 0))
b_target = np.empty((0, ))
assert_array_equal(A, A_target)
assert_array_equal(b, b_target)
# TODO: Test RM06 Simulation
state = np.random.get_state()
rng = Normal()
sim_data = rm06.simulate(parameters, self.T, rng.simulate([]))
assert_equal(rm06.num_params, 0)
assert_equal(rm06.name, 'RiskMetrics2006')
def test_errors(self):
with pytest.raises(ValueError):
GARCH(p=-1)
with pytest.raises(ValueError):
GARCH(o=-1)
with pytest.raises(ValueError):
GARCH(q=-1)
with pytest.raises(ValueError):
GARCH(p=0, q=0)
with pytest.raises(ValueError):
GARCH(power=-0.5)
with pytest.raises(ValueError):
EWMAVariance(lam=-0.5)
with pytest.raises(ValueError):
RiskMetrics2006(tau0=1, tau1=10)
with pytest.raises(ValueError):
RiskMetrics2006(tau0=1, tau1=10)
with pytest.raises(ValueError):
RiskMetrics2006(tau1=-10)
with pytest.raises(ValueError):
RiskMetrics2006(tau0=10, tau1=8, rho=1.5)
with pytest.raises(ValueError):
RiskMetrics2006(kmax=0)
with pytest.raises(ValueError):
RiskMetrics2006(rho=0.5)
y[::29] = np.nan
with pytest.raises(ValueError, match="NaN or inf values"):
arch_model(y)
y = np.random.standard_normal(1000)
y[::53] = np.inf
with pytest.raises(ValueError, match="NaN or inf values"):
arch_model(y)
y[::29] = np.nan
y[::53] = np.inf
with pytest.raises(ValueError, match="NaN or inf values"):
arch_model(y)
@pytest.mark.parametrize("first_obs", [None, 250])
@pytest.mark.parametrize("last_obs", [None, 2750])
@pytest.mark.parametrize("vol", [RiskMetrics2006(), EWMAVariance()])
def test_parameterless_fit(first_obs, last_obs, vol):
base = ConstantMean(SP500, volatility=vol)
base_res = base.fit(first_obs=first_obs, last_obs=last_obs, disp="off")
mod = ZeroMean(SP500, volatility=vol)
res = mod.fit(first_obs=first_obs, last_obs=last_obs, disp="off")
assert res.conditional_volatility.shape == base_res.conditional_volatility.shape
def test_invalid_vol_dist():
with pytest.raises(TypeError, match="volatility must inherit"):
ConstantMean(SP500, volatility="GARCH")
with pytest.raises(TypeError, match="distribution must inherit"):
ConstantMean(SP500, distribution="Skew-t")
