def roll_prediction_egarch(return_tr, vol_tr, return_ts, vol_ts,
training_order, method):
roll_x = return_tr
vol_hat = []
for i in range(len(vol_ts)):
print 'now processing', i
tmp_x = roll_x[-training_order:]
if method == 'garch':
model = pf.GARCH(tmp_x, p=1, q=1)
x = model.fit()
elif method == 'egarch':
model = pf.EGARCH(tmp_x, p=1, q=1)
x = model.fit()
vol_hat.append(np.asarray(model.predict(1))[0][0])
roll_x = np.concatenate((roll_x, return_ts[i:i + 1]))
# return rooted mse
return vol_hat, sqrt(
mean((vol_ts - np.asarray(vol_hat)) * (vol_ts - np.asarray(vol_hat))))
def test_lev_bbvi():
model = pf.EGARCH(data=data, p=1, q=1)
model.add_leverage()
x = model.fit('BBVI', iterations=100)
assert (len(model.latent_variables.z_list) == 6)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_lev_predict_nans():
model = pf.EGARCH(data=data, p=2, q=2)
model.add_leverage()
x = model.fit()
x.summary()
assert (len(
model.predict(h=5).values[np.isnan(model.predict(h=5).values)]) == 0)
def test_predict_is_nans():
model = pf.EGARCH(data=data, q=2, p=2)
x = model.fit()
x.summary()
assert (len(
model.predict_is(h=5).values[np.isnan(
model.predict_is(h=5).values)]) == 0)
def test_lev_sample_model():
model = pf.EGARCH(data=data, q=2, p=2)
model.add_leverage()
x = model.fit('BBVI', iterations=100)
sample = model.sample(nsims=100)
assert (sample.shape[0] == 100)
assert (sample.shape[1] == len(data) - 2)
def test_lev_couple_terms():
model = pf.EGARCH(data=data, p=1, q=1)
model.add_leverage()
x = model.fit()
assert (len(model.latent_variables.z_list) == 6)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_bbvi_mini_batch_elbo():
model = pf.EGARCH(data=data, p=1, q=1)
x = model.fit('BBVI',
iterations=300,
map_start=False,
mini_batch=32,
record_elbo=True)
assert (x.elbo_records[-1] > x.elbo_records[0])
def test_predict_is_intervals_mh():
model = pf.EGARCH(data=data, q=1, p=1)
x = model.fit('M-H', nsims=400)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values +
0.000001 >= predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values +
0.000001 >= predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values +
0.000001 >= predictions['1% Prediction Interval'].values))
def test_predict_is_intervals():
model = pf.EGARCH(data=data, q=2, p=2)
x = model.fit()
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values +
0.000001 >= predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values +
0.000001 >= predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values +
0.000001 >= predictions['1% Prediction Interval'].values))
def test_lev_predict_is_intervals_bbvi():
model = pf.EGARCH(data=data, q=1, p=1)
model.add_leverage()
x = model.fit('BBVI', iterations=100)
predictions = model.predict_is(h=10, intervals=True)
assert (np.all(predictions['99% Prediction Interval'].values +
0.000001 >= predictions['95% Prediction Interval'].values))
assert (np.all(predictions['95% Prediction Interval'].values +
0.000001 >= predictions['5% Prediction Interval'].values))
assert (np.all(predictions['5% Prediction Interval'].values +
0.000001 >= predictions['1% Prediction Interval'].values))
def oneshot_prediction_egarch(return_tr, vol_tr, return_ts, vol_ts, method):
if method == 'garch1':
model = pf.GARCH(return_tr, p=1, q=1)
x = model.fit()
tr_sigma2, _, ___ = model._model(model.latent_variables.get_z_values())
vol_tr_hat = tr_sigma2**0.5
elif method == 'egarch1':
model = pf.EGARCH(return_tr, p=1, q=1)
x = model.fit()
tr_sigma2, _, ___ = model._model(model.latent_variables.get_z_values())
vol_tr_hat = np.exp(tr_sigma2 / 2.0)
tmp_pre = np.asarray(model.predict(len(vol_ts)))
vol_ts_hat = []
for i in tmp_pre:
vol_ts_hat.append(i[0])
return vol_ts_hat, vol_tr_hat, sqrt(mean((vol_ts - np.asarray(vol_ts_hat))*(vol_ts - np.asarray(vol_ts_hat)))), \
sqrt(mean((vol_tr[1:] - np.asarray(vol_tr_hat))*(vol_tr[1:] - np.asarray(vol_tr_hat))))
def test_predict_is_length():
model = pf.EGARCH(data=data, p=2, q=2)
x = model.fit()
assert (model.predict_is(h=5).shape[0] == 5)
def test_pml():
model = pf.EGARCH(data=data, p=1, q=1)
x = model.fit('PML')
assert (len(model.latent_variables.z_list) == 5)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_lev_predict_length():
model = pf.EGARCH(data=data, p=2, q=2)
model.add_leverage()
x = model.fit()
x.summary()
assert (model.predict(h=5).shape[0] == 5)
def test_no_terms():
model = pf.EGARCH(data=data, p=0, q=0)
x = model.fit()
assert (len(model.latent_variables.z_list) == 3)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
def test_lev_bbvi_elbo():
model = pf.EGARCH(data=data, p=1, q=1)
model.add_leverage()
x = model.fit('BBVI', iterations=300, map_start=False, record_elbo=True)
assert (x.elbo_records[-1] > x.elbo_records[0])
def bte(self, topic_index=0):
model = pf.EGARCH(self.topic_counts[topic_index], p=1, q=1)
x = model.fit()
x.summary()
model.plot_fit()
def test_predict_nonconstant():
model = pf.EGARCH(data=data, p=2, q=2)
x = model.fit()
predictions = model.predict(h=10, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_lev_predict_is_nonconstant():
model = pf.EGARCH(data=data, p=2, q=2)
model.add_leverage()
x = model.fit()
predictions = model.predict_is(h=5, intervals=False)
assert (not np.all(predictions.values == predictions.values[0]))
def test_lev_ppc():
model = pf.EGARCH(data=data, q=2, p=2)
model.add_leverage()
x = model.fit('BBVI', iterations=100)
p_value = model.ppc()
assert (0.0 <= p_value <= 1.0)
def test_bbvi_mini_batch():
model = pf.EGARCH(data=data, p=1, q=1)
x = model.fit('BBVI', iterations=100, map_start=False, mini_batch=32)
assert (len(model.latent_variables.z_list) == 5)
lvs = np.array([i.value for i in model.latent_variables.z_list])
assert (len(lvs[np.isnan(lvs)]) == 0)
