# bashtage/arch

ARCH models in Python

Repository: https://github.com/bashtage/arch
Canonical: https://ross.abutalabs.com/products/bashtage-arch
Homepage: http://bashtage.github.io/arch/
Language: Python
License: NOASSERTION
License Family: other
Topics: arch, volatility, bootstrap, multiple-comparison-procedures, forecasting, risk, financial-econometrics, time-series, unit-root, dickey-fuller, adf, phillips-perron, df-gls, reality-check, spa, model-confidence-set, finance, variance, garch
Last push: 2026-08-10T04:34:30+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 21, longevity 100
- inputs: {"age_days": 4387, "days_push": 23, "days_rel": 316, "gap_med": 195.5, "n_releases_24m": 3}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1555, forks 290 (observed 2026-08-28T04:05:03.048805+00:00)

## What it is
A Python library for financial econometrics providing ARCH/GARCH volatility models, unit root tests, cointegration analysis, bootstrapping, and multiple comparison procedures. It uses Cython and Numba for performance and is actively maintained with regular releases.

## Use cases
- fit GARCH models to estimate asset return volatility
- forecast financial time series variance and risk
- run Dickey-Fuller and Phillips-Perron unit root tests
- bootstrap confidence intervals for time series statistics
- perform model confidence set and reality check multiple comparison procedures
- estimate long-run covariance of financial data
- test and estimate cointegration relationships

## When to choose
- you need ARCH/GARCH volatility modeling in Python
- you want econometric unit root or cointegration tests alongside volatility models
- you need fast, well-tested bootstrap methods for time series
- you are doing academic or applied financial econometrics research

## When to avoid
- you need multivariate volatility models like DCC-GARCH
- you want general-purpose machine learning forecasting rather than econometric models
- you need a full statistical stack rather than a specialized toolbox
- you work outside Python

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, math
- domain: data-science, fintech, time-series, mathematics
- platform: python
- tags: econometrics, garch, volatility-modeling, time-series, bootstrap, unit-root-tests, finance, forecasting

## Member repositories
- bashtage/arch (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:03.048805+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:00.938912+00:00, confidence not recorded.
  - readme: https://github.com/bashtage/arch (fetched 2026-08-28T04:05:03.048805+00:00, sha 81ba7c0dc14b)
  - homepage: http://bashtage.github.io/arch/ (fetched 2026-08-29T11:30:00.558170+00:00, sha 26ffc8ba633d)
- Data as of 2026-08-30T08:39:29.467469+00:00.