ARCH models in Python
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README.md

ARCH

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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Module Contents

Documentation

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.

Contributing

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

Examples

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
    • Generalized Error Distribution

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

import datetime as dt
import pandas.io.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()

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.

Bootstrap

  • 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 pandas.io.data 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.

Requirements

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

  • Python (2.7, 3.5 - 3.7)
  • NumPy (1.13+)
  • SciPy (0.19+)
  • Pandas (0.21+)
  • statsmodels (0.8+)
  • matplotlib (2.0+)

Optional Requirements

  • Numba (0.35+) will be used if available and when installed using the --no-binary option
  • IPython (5.0+) is required to run the notebooks

Installing

  • 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.

Linux/OSX

pip install git+https://github.com/bashtage/arch.git

Anaconda

Anaconda builds are not currently available for OSX.

conda install arch -c bashtage

Windows

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+https://github.com/bashtage/arch.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+https://github.com/bashtage/arch.git --install-option "--no-binary"

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