Metadata-Version: 2.1
Name: mgarch
Version: 0.2.1
Summary: DCC-GARCH(1,1)
Home-page: https://github.com/srivastavaprashant/mgarch
Author: Prashant Srivastava
Author-email: srivastava.prashant898@gmail.com
License: MIT
Download-URL: https://github.com/srivastavaprashant/mgarch/archive/0.2.0.tar.gz
Keywords: volatility,multivariate,garch
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy

# mgarch

mgarch is a python package for predicting volatility of daily returns in financial markets. 

DCC-GARCH(1,1) for multivariate normal and student t distribution.


## Use case:
For Multivariate Normal Distribution
```python
# shape(rt) = (t, n) numpy matrix with t days of observation and n number of assets
import mgarch
vol = mgarch.mgarch()
vol.fit(rt)
ndays = 10 # volatility of nth day
cov_nextday = vol.predict(ndays)
```

For Multivariate Student-t Distribution
```python
# shape(rt) = (t, n) numpy matrix with t days of observation and n number of assets
import mgarch
dist = 't'
vol = mgarch.mgarch(dist)
vol.fit(rt)
ndays = 10 # volatility of nth day
cov_nextday = vol.predict(ndays)
```



## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
Academic Free License v3.0


