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5841846 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import numpy as np
import pandas as pd
from arch import arch_model
from sklearn.preprocessing import StandardScaler
class LSGarchModel:
"""
LS-GARCH(1,1) model with Student-t distribution for tail risk estimation.
Uses OLS mean equation with exogenous features scaled for optimization stability.
"""
def __init__(self, p: int = 1, q: int = 1, dist: str = 'studentst'):
self.p = p
self.q = q
self.dist = dist
self.scaler = StandardScaler()
self.model = None
self.results = None
self.params = None
self.omega = None
self.alpha = None
self.beta = None
self.nu = None
def fit(self, X: pd.DataFrame, y: pd.Series):
"""Fits the GARCH model. Scales X to prevent ML optimizer overflows."""
X_scaled = pd.DataFrame(
self.scaler.fit_transform(X),
columns=X.columns,
index=X.index
)
self.model = arch_model(
y,
x=X_scaled,
mean='LS',
vol='Garch',
p=self.p,
q=self.q,
dist=self.dist
)
self.results = self.model.fit(disp='off')
# Save key params
self.params = self.results.params
self.omega = self.params.get('omega', 0.0)
self.alpha = self.params.get('alpha[1]', 0.0)
self.beta = self.params.get('beta[1]', 0.0)
self.nu = self.params.get('nu', None)
return self
def predict(self, X: pd.DataFrame) -> pd.Series:
"""Predicts the mean equation (mu_t) out-of-sample using exogenous features."""
if self.results is None:
raise ValueError("Model is not fitted yet. Call fit() first.")
X_scaled = pd.DataFrame(
self.scaler.transform(X),
columns=X.columns,
index=X.index
)
coeffs = self.params[X_scaled.columns]
const = self.params.get('Const', 0.0)
return X_scaled.dot(coeffs) + const
def summary(self) -> str:
"""Returns the summary text of the GARCH model."""
if self.results is None:
return "Model not fitted yet."
return self.results.summary().as_text()
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