Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

ARCH for Python

Project Description


Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python (with Cython and/or Numba used to improve performance)


Released documentation is hosted on read the docs. Current documentation from the master branch is hosted on my github pages.

More about ARCH

More information about ARCH and related models is available in the notes and research available at Kevin Sheppard’s site.


Contributions are welcome. There are opportunities at many levels to contribute:

  • Implement new volatility process, e.g FIGARCH
  • Improve docstrings where unclear or with typos
  • Provide examples, preferably in the form of IPython notebooks


Volatility Modeling

  • Mean models
    • Constant mean
    • Heterogeneous Autoregression (HAR)
    • Autoregression (AR)
    • Zero mean
    • Models with and without exogenous regressors
  • Volatility models
    • ARCH
    • GARCH
    • TARCH
    • EGARCH
    • EWMA/RiskMetrics
  • Distributions
    • Normal
    • Student’s T

See the univariate volatility example notebook for a more complete overview.

import datetime as dt
import as web
st = dt.datetime(1990,1,1)
en = dt.datetime(2014,1,1)
data = web.get_data_yahoo('^FTSE', start=st, end=en)
returns = 100 * data['Adj Close'].pct_change().dropna()

from arch import arch_model
am = arch_model(returns)
res =

Unit Root Tests

  • Augmented Dickey-Fuller
  • Dickey-Fuller GLS
  • Phillips-Perron
  • KPSS
  • Variance Ratio tests

See the unit root testing example notebook for examples of testing series for unit roots.


  • Bootstraps
    • IID Bootstrap
    • Stationary Bootstrap
    • Circular Block Bootstrap
    • Moving Block Bootstrap
  • Methods
    • Confidence interval construction
    • Covariance estimation
    • Apply method to estimate model across bootstraps
    • Generic Bootstrap iterator

See the bootstrap example notebook for examples of bootstrapping the Sharpe ratio and a Probit model from Statsmodels.

# Import data
import datetime as dt
import pandas as pd
import as web
start = dt.datetime(1951,1,1)
end = dt.datetime(2014,1,1)
sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)
start = sp500.index.min()
end = sp500.index.max()
monthly_dates = pd.date_range(start, end, freq='M')
monthly = sp500.reindex(monthly_dates, method='ffill')
returns = 100 * monthly['Adj Close'].pct_change().dropna()

# Function to compute parameters
def sharpe_ratio(x):
    mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())
    return np.array([mu, sigma, mu / sigma])

# Bootstrap confidence intervals
from arch.bootstrap import IIDBootstrap
bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')

Multiple Comparison Procedures

  • Test of Superior Predictive Ability (SPA), also known as the Reality Check or Bootstrap Data Snooper
  • Stepwise (StepM)
  • Model Confidence Set (MCS)

See the multiple comparison example notebook for examples of the multiple comparison procedures.


These requirements reflect the testing environment. It is possible that arch will work with older versions.

  • Python (2.7, 3.4 - 3.6)
  • NumPy (1.10+)
  • SciPy (0.16+)
  • Pandas (0.16+)
  • statsmodels (0.6+)
  • matplotlib (1.5+)

Optional Requirements

  • Numba (0.24+) will be used if available and when installed using the –no-binary option
  • IPython (4.0+) is required to run the notebooks


  • Cython (0.24+, if not using –no-binary)
  • py.test (For tests)
  • sphinx (to build docs)
  • guzzle_sphinx_theme (to build docs)
  • ipython (to build docs)
  • numpydoc (to build docs)

Note: Setup does not verify requirements. Please ensure these are installed.


pip install git+


Anaconda builds are not currently available for OSX.

conda install arch -c bashtage


Building extension using the community edition of Visual Studio is well supported for Python 3.5+. Building extensions for 64-bit Windows for use in Python 2.7 is also supported using Microsoft Visual C++ Compiler for Python 2.7. Building on other combinations of Python/Windows is more difficult and is not necessary when Numba is installed since just-in-time compiled code (Numba) runs as fast as ahead-of-time compiled extensions.

With a compiler

If you are comfortable compiling binaries on Windows:

pip install git+

No Compiler

All binary code is backed by a pure Python implementation. Compiling can be skipped using the flag --no-binary

pip install git+ --install-option "--no-binary"

Note: If Cython is not installed, the package will be installed as-if –no-binary was used.

Release History

This version
History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(514.8 kB) Copy SHA256 Hash SHA256
Wheel 2.7 Dec 14, 2017
(504.0 kB) Copy SHA256 Hash SHA256
Wheel 2.7 Dec 14, 2017
(498.5 kB) Copy SHA256 Hash SHA256
Wheel 3.5 Dec 14, 2017
(494.3 kB) Copy SHA256 Hash SHA256
Wheel 3.5 Dec 14, 2017
(499.6 kB) Copy SHA256 Hash SHA256
Wheel 3.6 Dec 14, 2017
(502.9 kB) Copy SHA256 Hash SHA256
Wheel 3.6 Dec 14, 2017
(363.0 kB) Copy SHA256 Hash SHA256
Source None Dec 14, 2017

Supported By

Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Google Google Cloud Servers