This is a work-in-progress for ARCH and other tools for financial econometrics, written in Python (and Cython)

• Univariate ARCH Models
• Unit Root Tests
• Bootstrapping
• Multiple Comparison Tests

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

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()

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

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')    

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

• NumPy (1.7+)
• SciPy (0.12+)
• Pandas (0.14+)
• statsmodels (0.5+)
• matplotlib (1.3+)

• Numba (0.15+) will be used if available and when installed using the --no-binary option
• IPython (3.0+) is required to run the notebooks

• Cython (0.20+, if not using --no-binary)
• nose (For tests)
• sphinx (to build docs)
• sphinx-napoleon (to build docs)

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

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

Anaconda

Anaconda builds are not currently available for OSX.

conda install -c https://conda.binstar.org/bashtage arch

With a compiler

If you are comfortable compiling binaries on Windows:

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

Note: the test suite compares the Numba implementations against Cython implementations of some recursions, and so it is not possible to run the test suite when installing with --no-binary .

Anaconda

conda install -c https://conda.binstar.org/bashtage arch