Metadata-Version: 1.1
Name: arch
Version: 3.1
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: |Documentation Status| |CI Status| |Coverage Status| |DOI|
        
        ARCH
        ====
        
        This is a work-in-progress for ARCH and other tools for financial
        econometrics, written in Python (and Cython)
        
        What is in this repository?
        ---------------------------
        
        -  `Univariate ARCH Models <#volatility>`__
        -  `Unit Root Tests <#unit-root>`__
        -  `Bootstrapping <#bootstrap>`__
        -  `Multiple Comparison Tests <#multiple-comparison>`__
        
        Documentation
        -------------
        
        Documentation is hosted on `read the
        docs <http://arch.readthedocs.org/en/latest/>`__
        
        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
        
        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
        -  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
        ------------
        
        -  NumPy (1.7+)
        -  SciPy (0.12+)
        -  Pandas (0.14+)
        -  statsmodels (0.5+)
        -  matplotlib (1.3+)
        
        Optional Requirements
        ~~~~~~~~~~~~~~~~~~~~~
        
        -  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
        
        Installing
        ~~~~~~~~~~
        
        -  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.
        
        Linux/OSX
        ~~~~~~~~~
        
        ::
        
            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
        
        Windows
        ~~~~~~~
        
        **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
        
        .. |Documentation Status| image:: https://readthedocs.org/projects/arch/badge/?version=latest
           :target: http://arch.readthedocs.org/en/latest/
        .. |CI Status| image:: https://travis-ci.org/bashtage/arch.svg?branch=master
           :target: https://travis-ci.org/bashtage/arch
        .. |Coverage Status| image:: https://coveralls.io/repos/bashtage/arch/badge.svg?branch=master
           :target: https://coveralls.io/r/bashtage/arch?branch=master
        .. |DOI| image:: https://zenodo.org/badge/doi/10.5281/zenodo.15681.svg
           :target: http://dx.doi.org/10.5281/zenodo.15681
        
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 :: 4 - Beta
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
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
