-
Notifications
You must be signed in to change notification settings - Fork 245
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
3 changed files
with
182 additions
and
6 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,167 @@ | ||
import datetime as dt | ||
Check notice Code scanning / CodeQL Unused import Note test
Import of 'dt' is not used.
|
||
from functools import partial | ||
Check notice Code scanning / CodeQL Unused import Note test
Import of 'partial' is not used.
|
||
from itertools import product | ||
|
||
import numpy as np | ||
from numpy.random import RandomState | ||
from numpy.testing import assert_allclose | ||
import pandas as pd | ||
import pytest | ||
|
||
from arch.data import sp500 | ||
from arch.univariate import ( | ||
APARCH, | ||
ARX, | ||
EGARCH, | ||
FIGARCH, | ||
GARCH, | ||
HARCH, | ||
HARX, | ||
LS, | ||
ConstantMean, | ||
ConstantVariance, | ||
EWMAVariance, | ||
MIDASHyperbolic, | ||
RiskMetrics2006, | ||
ZeroMean, | ||
arch_model, | ||
) | ||
|
||
SP500 = 100 * sp500.load()["Adj Close"].pct_change().dropna() | ||
N = SP500.shape[0] | ||
SP500_initial = SP500.iloc[: N // 2] | ||
SP500_append = SP500.iloc[N // 2 :] | ||
|
||
|
||
class HARXWrapper(HARX): | ||
def __init__(self, y, x=None, volatility=None): | ||
super().__init__(y, lags=[1, 5], x=x, volatility=volatility) | ||
|
||
|
||
class ARXWrapper(ARX): | ||
def __init__(self, y, x=None, volatility=None): | ||
super().__init__(y, lags=2, x=x, volatility=volatility) | ||
|
||
|
||
MEAN_MODELS = [ | ||
HARXWrapper, | ||
ARXWrapper, | ||
ConstantMean, | ||
ZeroMean, | ||
] | ||
|
||
VOLATILITIES = [ | ||
ConstantVariance(), | ||
GARCH(), | ||
FIGARCH(), | ||
EWMAVariance(lam=0.94), | ||
MIDASHyperbolic(), | ||
HARCH(lags=[1, 5, 22]), | ||
RiskMetrics2006(), | ||
APARCH(), | ||
EGARCH(), | ||
] | ||
|
||
X_MEAN_MODELS = [HARXWrapper, ARXWrapper, LS] | ||
|
||
MODEL_SPECS = list(product(MEAN_MODELS, VOLATILITIES)) | ||
|
||
IDS = [f"{mean.__name__}-{str(vol).split('(')[0]}" for mean, vol in MODEL_SPECS] | ||
|
||
|
||
@pytest.fixture(params=MODEL_SPECS, ids=IDS) | ||
def mean_volatility(request): | ||
mean, vol = request.param | ||
return mean, vol | ||
|
||
|
||
def test_append(): | ||
mod = arch_model(SP500_initial) | ||
mod.append(SP500_append) | ||
res = mod.fit(disp="off") | ||
|
||
direct = arch_model(SP500) | ||
res_direct = direct.fit(disp="off") | ||
assert_allclose(res.params, res_direct.params, rtol=1e-5) | ||
assert_allclose(res.conditional_volatility, res_direct.conditional_volatility) | ||
assert_allclose(res.resid, res_direct.resid) | ||
assert_allclose(mod._backcast, direct._backcast) | ||
|
||
|
||
def test_alt_means(mean_volatility): | ||
mean, vol = mean_volatility | ||
mod = mean(SP500_initial, volatility=vol) | ||
mod.append(SP500_append) | ||
res = mod.fit(disp="off") | ||
|
||
direct = mean(SP500, volatility=vol) | ||
res_direct = direct.fit(disp="off") | ||
assert_allclose(res.conditional_volatility, res_direct.conditional_volatility) | ||
assert_allclose(res.resid, res_direct.resid) | ||
if mod._backcast is not None: | ||
assert_allclose(mod._backcast, direct._backcast) | ||
else: | ||
assert direct._backcast is None | ||
|
||
|
||
def test_append_scalar_no_reestiamtion(mean_volatility): | ||
mean, vol = mean_volatility | ||
mod = mean(np.asarray(SP500_initial), volatility=vol) | ||
for val in np.asarray(SP500_append): | ||
mod.append(val) | ||
|
||
|
||
def test_append_scalar_bad_value(): | ||
mod = HARX(SP500_initial, lags=[1, 5], volatility=GARCH()) | ||
with pytest.raises(TypeError): | ||
mod.append(SP500_append.iloc[0]) | ||
|
||
|
||
def test_append_type_mismatch(mean_volatility): | ||
mean, vol = mean_volatility | ||
mod = mean(SP500_initial, volatility=vol) | ||
with pytest.raises(TypeError, match="Input data must be the same"): | ||
mod.append(np.asarray(SP500_append)) | ||
with pytest.raises(TypeError, match="Input data must be the same"): | ||
mod.append(SP500_append.tolist()) | ||
|
||
mod_arr = mean(np.asarray(SP500_initial), volatility=vol) | ||
with pytest.raises(TypeError, match="Input data must be the same"): | ||
mod_arr.append(SP500_append) | ||
with pytest.raises(TypeError, match="Input data must be the same"): | ||
mod_arr.append(SP500_append.tolist()) | ||
|
||
mod_list = mean(SP500_initial.tolist(), volatility=vol) | ||
with pytest.raises(TypeError, match="Input data must be the same"): | ||
mod_list.append(SP500_append) | ||
with pytest.raises(TypeError, match="Input data must be the same"): | ||
mod_list.append(np.asarray(SP500_append)) | ||
|
||
|
||
def test_append_x_type_mismatch(): | ||
pass | ||
|
||
|
||
@pytest.mark.parametrize("mean", X_MEAN_MODELS) | ||
def test_bad_append_model_with_exog(mean): | ||
mod = mean(SP500_initial, volatility=GARCH()) | ||
x = pd.DataFrame( | ||
np.random.randn(SP500_append.shape[0], 2), | ||
columns=["a", "b"], | ||
index=SP500_append.index, | ||
) | ||
with pytest.raises(ValueError, match=""): | ||
mod.append(SP500_append, x=x) | ||
|
||
x_initial = pd.DataFrame( | ||
np.random.randn(SP500_initial.shape[0], 2), | ||
columns=["a", "b"], | ||
index=SP500_initial.index, | ||
) | ||
mod = mean(SP500_initial, x=x_initial, volatility=GARCH()) | ||
with pytest.raises(ValueError, match=""): | ||
mod.append(SP500_append) | ||
|
||
|
||
def test_bad_append_ls(): | ||
pass |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters