---
id: arch
version: "8.0.0"
license: NCSA
license_treatment: unclear
maintenance: active
---

# arch — ARCH for Python

License: unclear · Maintenance: active · Downloads: 1.1M/mo

## What it is and what it does

arch is a Python library for financial econometrics, specializing in volatility modeling and time-series analysis. It implements ARCH and GARCH models (and variants like TARCH, EGARCH, EWMA) for capturing changing volatility in financial returns, alongside classical econometric tests like Augmented Dickey-Fuller, Phillips-Perron, and KPSS for unit roots. The package also provides cointegration testing (Engle-Granger, Phillips-Ouliaris), multiple comparison procedures (SPA, StepM, MCS), and bootstrap methods (IID, stationary, block-based) for confidence interval construction and resampling-based inference.

The library is built on numpy, pandas, scipy, and statsmodels, with optional Cython and Numba acceleration for performance. It targets researchers, practitioners in finance and insurance, and quantitative analysts who need to model time-varying volatility, test for stationarity, or construct robust confidence intervals for financial statistics. The package is actively maintained, supports modern Python versions (3.10–3.13), and has been in development since 2014.

Use it for:

- Model conditional volatility in stock returns using GARCH to forecast risk and value-at-risk
- Test whether economic time series have unit roots before fitting regression models
- Estimate long-run covariance matrices for Newey-West corrected standard errors in econometric models
- Bootstrap confidence intervals for Sharpe ratios or other portfolio performance metrics
- Test for cointegration between pairs of financial assets to identify mean-reversion trading signals
- Compare predictive models using multiple comparison procedures to avoid data-snooping bias

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides ARCH/GARCH volatility models, unit root tests, cointegration analysis, bootstrapping methods, and long-run covariance estimation for financial time series analysis.

Yes. arch is a mature, actively maintained library (1551 GitHub stars, top 5000 PyPI by downloads) with no known vulnerabilities, permissive licensing, and a focused feature set for financial econometrics. Install friction is moderate but manageable; prebuilt wheels are available for all major platforms and Python versions. Recommended for anyone doing volatility modeling, unit root testing, or bootstrap-based inference on financial time series.

## Install

pip install arch
uv add arch
poetry add arch

## Installing arch

Before you install: Medium install friction due to compiled components (Cython/Numba). Actively maintained with recent releases; supports Python 3.10–3.13 with prebuilt wheels across major platforms. Five runtime dependencies (numpy, pandas, scipy, statsmodels, packaging) are standard in data science environments.

License in practice: Licensed under NCSA, which is permissive and compatible with most commercial and open-source projects. No notable restrictions on use or redistribution.

Quickstart:

pip install arch

from arch import arch_model
import pandas as pd

returns = pd.Series([...])  # your returns data
am = arch_model(returns)
res = am.fit()

Requires Python 3.10+. Optional Numba acceleration requires setting ARCH_NO_BINARY=1 environment variable and installing from source.

Verify before relying:

- Whether Numba acceleration provides meaningful performance gains for typical use cases
- Specific volatility model types beyond ARCH, GARCH, TARCH, EGARCH, and EWMA that are implemented

## Package facts

- License: NCSA (unclear)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 1.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ARCH GARCH volatility modeling, unit root testing econometrics, financial time series analysis, bootstrap confidence intervals, cointegration testing, long-run covariance estimation, heteroskedasticity models, econometrics, volatility-modeling, time-series

[View on SkillFed](https://skillfed.io/packages/arch) · [View on PyPI](https://pypi.org/project/arch/)