This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
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Project Description

ARCH

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

Documentation

Documentation is hosted on read the docs

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

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

  • NumPy (1.8+)
  • SciPy (0.15+)
  • Pandas (0.15+)
  • statsmodels (0.6+)
  • matplotlib (1.3+)

Optional Requirements

  • Numba (0.21+) will be used if available and when installed using the –no-binary option
  • IPython (4.0+) is required to run the notebooks

Installing

  • Cython (0.20+, if not using –no-binary)
  • py.test (For tests)
  • sphinx (to build docs)
  • sphinx-napoleon (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 -c https://conda.binstar.org/bashtage arch

Windows

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: 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
Release History

Release History

3.2

This version

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Changelog content for this version goes here.

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3.1

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3.0

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2.0

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1.0

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
arch-3.2-cp27-cp27m-win_amd64.whl (228.9 kB) Copy SHA256 Checksum SHA256 2.7 Wheel Aug 4, 2016
arch-3.2-cp35-cp35m-win_amd64.whl (221.0 kB) Copy SHA256 Checksum SHA256 3.5 Wheel Aug 4, 2016
arch-3.2.tar.bz2 (79.1 kB) Copy SHA256 Checksum SHA256 Source Aug 4, 2016
arch-3.2.tar.gz (95.0 kB) Copy SHA256 Checksum SHA256 Source Aug 4, 2016
arch-3.2.zip (113.2 kB) Copy SHA256 Checksum SHA256 Source Aug 4, 2016

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