Metadata-Version: 1.2
Name: arch
Version: 4.10.0
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|
        
        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 3
        ~~~~~~~~
        
        ``arch`` is Python 3 only. Version 4.8 is the final version that
        supported Python 2.7.
        
        .. _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)
        -  sphinx_material (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.
        
        .. |arch| image:: https://bashtage.github.io/arch/doc/_static/images/color-logo-256.png
           :target: https://github.com/bashtage/arch
        .. |Travis Build Status| image:: https://travis-ci.org/bashtage/arch.svg?branch=master
           :target: https://travis-ci.org/bashtage/arch
        .. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/nmt02u7jwcgx7i2x?svg=true
           :target: https://ci.appveyor.com/project/bashtage/arch/branch/master
        .. |Documentation Status| image:: https://readthedocs.org/projects/arch/badge/?version=latest
           :target: http://arch.readthedocs.org/en/latest/
        .. |Coverage Status| image:: https://coveralls.io/repos/github/bashtage/arch/badge.svg?branch=master
           :target: https://coveralls.io/r/bashtage/arch?branch=master
        .. |codecov| image:: https://codecov.io/gh/bashtage/arch/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/bashtage/arch
        .. |Code Quality: Python| image:: https://img.shields.io/lgtm/grade/python/g/bashtage/arch.svg?logo=lgtm&logoWidth=18
           :target: https://lgtm.com/projects/g/bashtage/arch/context:python
        .. |Total Alerts| image:: https://img.shields.io/lgtm/alerts/g/bashtage/arch.svg?logo=lgtm&logoWidth=18
           :target: https://lgtm.com/projects/g/bashtage/arch/alerts
        .. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/cea43b588e0f4f2a9d8ba37cf63f8210
           :target: https://www.codacy.com/app/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade
        .. |codebeat badge| image:: https://codebeat.co/badges/18a78c15-d74b-4820-b56d-72f7e4087532
           :target: https://codebeat.co/projects/github-com-bashtage-arch-master
        .. |DOI| image:: https://zenodo.org/badge/23468876.svg
           :target: https://zenodo.org/badge/latestdoi/23468876
        
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 :: 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
Requires-Python: >=3.5
