mfe vs. arch vs. statsmodels¶

Decision guide¶

Task	Use
GARCH / EGARCH / TARCH / APARCH estimation	`arch`
FIGARCH, HARCH, MIDAS-GARCH	`arch`
Unit root tests (ADF, PP, KPSS, DFGLS)	`arch.unitroot`
Cointegration (Johansen, Engle-Granger)	`statsmodels`
ARMA/ARMAX estimation	`statsmodels`
VAR with robust VCV options	`mfe`
Granger causality with heteroskedastic VCV	`mfe`
Impulse response functions under heteroskedasticity	`mfe`
Realized variance / BPV / MedRV	`mfe`
Realized kernel (noise-robust)	`mfe`
TSRV / MSRV (two-scale noise correction)	`mfe`
Hayashi-Yoshida non-synchronous covariance	`mfe`
Multivariate realized kernel (PSD guaranteed)	`mfe`
DCC-GARCH	`mfe`
BEKK-GARCH	`mfe`
CCC-GARCH	`mfe`
GO-GARCH / O-GARCH	`mfe`
RCC (Rotated Conditional Correlation)	`mfe`
HAR-RV model	`mfe`
HEAVY model (realized variance in mean equation)	`mfe`
Beveridge-Nelson decomposition	`mfe`
SPA test / StepM FWER	`mfe`
Wild bootstrap for realized volatility	`mfe`
Fama-MacBeth regression	`mfe`
OLS with White / Newey-West SEs	`mfe` (thin wrapper) or `statsmodels`
PCA with financial conventions	`mfe`
Hansen Skew-t / GED distributions (standalone)	`mfe`

Key API differences¶

arch (univariate, correct home for GARCH)¶

from arch import arch_model
am = arch_model(returns, vol="Garch", p=1, q=1)
res = am.fit()

mfe (multivariate, realized, everything arch doesn't cover)¶

from mfe.multivariate import DCC
from mfe.realized import realized_kernel

dcc = DCC().fit(returns)          # (T, K) → (T, K, K) sigma_t
rk = realized_kernel(r)           # noise-robust RV

statsmodels (ARIMA, VAR basic, cointegration)¶

from statsmodels.tsa.api import VAR
mod = VAR(data)
res = mod.fit(maxlags=2)
# → no robust VCV, no GC test with het-robust options

from mfe.timeseries import vectorar, grangercause
res = vectorar(data, lags=2, het=True)          # White-robust VCV
gc  = grangercause(data, lags=2, method="wald") # Wald test with robust VCV

What mfe does NOT replace¶

arch for any univariate GARCH estimation — we use arch internally for the first step of DCC/RCC/CCC.
statsmodels for ARIMA, SARIMA, SARIMAX estimation.
scipy.signal for filtering.
sklearn.decomposition.PCA for general-purpose PCA (our PCA is tailored to financial returns with covariance convention and factor interpretation tools).