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

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• Univariate ARCH Models
• Unit Root Tests
• Cointegration Testing and Analysis
• Bootstrapping
• Multiple Comparison Tests
• Long-run Covariance Estimation

arch is Python 3 only. Version 4.8 is the final version that supported Python 2.7.

Documentation from the main branch is hosted on my github pages.

Released documentation is hosted on read the docs.

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

• 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
  • Generalized Error Distribution

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

import datetime as dt
import pandas_datareader.data 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 = am.fit()

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

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

• Tests
  • Engle-Granger Test