ARCH for Python
Project description
ARCH
Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python (with Cython and/or Numba used to improve performance)
What is in this repository?
Documentation
Released documentation is hosted on read the docs. Current documentation from the master branch is hosted on my github pages.
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
These requirements reflect the testing environment. It is possible that arch will work with older versions.
Python (2.7, 3.4 - 3.6)
NumPy (1.10+)
SciPy (0.16+)
Pandas (0.16+)
statsmodels (0.6+)
matplotlib (1.5+)
Optional Requirements
Numba (0.24+) will be used if available and when installed using the –no-binary option
IPython (4.0+) is required to run the notebooks
Installing
Cython (0.24+, if not using –no-binary)
py.test (For tests)
sphinx (to build docs)
guzzle_sphinx_theme (to build docs)
ipython (to build docs)
numpydoc (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 arch -c bashtage
Windows
Building extension using the community edition of Visual Studio is well supported for Python 3.5+. Building extensions for 64-bit Windows for use in Python 2.7 is also supported using Microsoft Visual C++ Compiler for Python 2.7. 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.
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: If Cython is not installed, the package will be installed as-if –no-binary was used.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for arch-4.3.1-cp36-cp36m-win_amd64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 86f6d6257dbe348ae06406a534efc54ce58ac3fb00ecff857341fb615e2ba700 |
|
| MD5 | 2a8ee3d2164a23346db1dcbba9b32f0e |
|
| BLAKE2b-256 | 8f47f7ca7d8b296ba23f2d417aca77598e15de62b8b3b97e0e1614d269640159 |
Hashes for arch-4.3.1-cp36-cp36m-macosx_10_7_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f8cbc00c33c4a215339d0e8594fc8ab69368f87b5d8c3d4cf76d9728141a33d3 |
|
| MD5 | b57e8f711e3df9be7014e013a20f9f70 |
|
| BLAKE2b-256 | bfcfc336a092106c01901b33aec6ff89dab2eba0be617077ea1f6fc39c7e60cd |
Hashes for arch-4.3.1-cp35-cp35m-win_amd64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | af7097dc98f74783eb356f651223e78356c6ee191dd646d6e6a9830d2c9a6f33 |
|
| MD5 | f5e964180fc90a72e57e6ff59ce19489 |
|
| BLAKE2b-256 | f0b497fac73f4a19d61c1c4c6d96892fbe0a335c69b5b6c0ffbb65a626de7b2c |
Hashes for arch-4.3.1-cp35-cp35m-macosx_10_6_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 4529586d739ae6371d18a043bf144b29040bf506c2917eef0891b400f3d44a5e |
|
| MD5 | 0f79b5a2469336abb713ec7513d54fea |
|
| BLAKE2b-256 | 7e3ca0450148d373408ae6ba9bb75275eb72d706fd4eb52a567846299015856b |
Hashes for arch-4.3.1-cp27-cp27m-win_amd64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 3ec4b64e6812356d0df44ddbf1236644344f8645fd7aa2288ee60e1cbeee0b19 |
|
| MD5 | de964bff3e4209612365185c1a5448c1 |
|
| BLAKE2b-256 | 8fc3040c994067fdbdc232a48090ce0fe3f21a5fc78efb85000ae4240b8668c9 |
Hashes for arch-4.3.1-cp27-cp27m-macosx_10_6_x86_64.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | cf533ff637c4635f25f25e4193540f3a01f9f485d32622cc5bb3019d17169e6c |
|
| MD5 | aff9d72551e6e3dc044072883e705179 |
|
| BLAKE2b-256 | 7abada87289c918168535ba5906da31fc88860389b69202696548314afdffbbc |
