Metadata-Version: 1.1
Name: arch
Version: 4.8.1
Summary: ARCH for Python
Home-page: http://github.com/bashtage/arch
Author: Kevin Sheppard
Author-email: kevin.sheppard@economics.ox.ac.uk
License: NCSA
Description: ARCH
        ====
        
        Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
        financial econometrics, written in Python (with Cython and/or Numba used
        to improve performance)
        
        Continuous Integration
                              
        
        |Travis Build Status| |Appveyor Build Status|
        
        Documentation
                     
        
        |Documentation Status|
        
        Coverage
                
        
        |Coverage Status| |codecov|
        
        Code Inspections
                        
        
        |Code Quality: Python| |Total Alerts| |Codacy Badge| |codebeat badge|
        
        Citation
                
        
        |DOI|
        
        Module Contents
        ---------------
        
        -  `Univariate ARCH Models <#volatility>`__
        -  `Unit Root Tests <#unit-root>`__
        -  `Bootstrapping <#bootstrap>`__
        -  `Multiple Comparison Tests <#multiple-comparison>`__
        
        Python 2.7 Support
        ~~~~~~~~~~~~~~~~~~
        
        Version 4.8 is the final version that officially supports or is tested
        on Python 2.7, and is the final version that has Python 2.7 wheels. It
        is time to move to Python 3.5+, and to enjoy the substantial improvement
        available in recent Python releases.
        
        .. _documentation-1:
        
        Documentation
        -------------
        
        Released documentation is hosted on `read the
        docs <http://arch.readthedocs.org/en/latest/>`__. Current documentation
        from the master branch is hosted on `my github
        pages <http://bashtage.github.io/arch/doc/index.html>`__.
        
        More about ARCH
        ---------------
        
        More information about ARCH and related models is available in the notes
        and research available at `Kevin Sheppard's
        site <http://www.kevinsheppard.com>`__.
        
        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 <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/univariate_volatility_modeling.ipynb>`__
        for a more complete overview.
        
        .. code:: python
        
           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
        -  Zivot-Andrews
        -  Variance Ratio tests
        
        See the `unit root testing example
        notebook <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/unitroot_examples.ipynb>`__
        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 <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/bootstrap_examples.ipynb>`__
        for examples of bootstrapping the Sharpe ratio and a Probit model from
        Statsmodels.
        
        .. code:: python
        
           # 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 <http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/multiple-comparison_examples.ipynb>`__
        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 (3.5+)
        -  NumPy (1.13+)
        -  SciPy (0.19+)
        -  Pandas (0.21+)
        -  statsmodels (0.8+)
        -  matplotlib (2.0+), optional
        -  cached-property (1.5.1+), optional
        
        Optional Requirements
        ~~~~~~~~~~~~~~~~~~~~~
        
        -  Numba (0.35+) will be used if available **and** when installed using
           the --no-binary option
        -  jupyter and notebook are required to run the notebooks
        
        Installing
        ----------
        
        Standard installation with a compiler requires Cython. If you do not
        have a compiler installed, the ``arch`` should still install. You will
        see a warning but this can be ignored. If you don't have a compiler,
        ``numba`` is strongly recommended.
        
        pip
        ~~~
        
        Releases are available PyPI and can be installed with ``pip``.
        
        .. code:: bash
        
           pip install arch
        
        This command should work whether you have a compiler installed or not.
        If you want to install with the ``--no-binary`` options, use
        
        .. code:: bash
        
           pip install arch --install-option="--no-binary"
        
        You can alternatively install the latest version from GitHub
        
        .. code:: bash
        
           pip install git+https://github.com/bashtage/arch.git
        
        ``--install-option="--no-binary"`` can be used to disable compilation of
        the extensions.
        
        Anaconda
        ~~~~~~~~
        
        ``conda`` users can install from my channel,
        
        .. code:: bash
        
           conda install arch -c bashtage
        
        Windows
        ~~~~~~~
        
        Building extension using the community edition of Visual Studio is well
        supported for Python 3.5+. 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.
        
        Developing
        ~~~~~~~~~~
        
        The development requirements are:
        
        -  Cython (0.24+, if not using --no-binary)
        -  py.test (For tests)
        -  sphinx (to build docs)
        -  guzzle_sphinx_theme (to build docs)
        -  jupyter, notebook and nbsphinx (to build docs)
        
        Installation Notes:
        ~~~~~~~~~~~~~~~~~~~
        
        1. If Cython is not installed, the package will be installed as-if
           ``--no-binary`` was used.
        2. Setup does not verify these requirements. Please ensure these are
           installed.
        
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           :target: https://travis-ci.org/bashtage/arch
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Keywords: arch,ARCH,variance,econometrics,volatility,finance,GARCH,bootstrap,random walk,unit root,Dickey Fuller,time series,confidence intervals,multiple comparisons,Reality Check,SPA,StepM
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Cython
Classifier: Topic :: Scientific/Engineering
