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    "info": {
        "author": "Daniel Bok",
        "author_email": "daniel.bok@outlook.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Education",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Education",
            "Topic :: Scientific/Engineering"
        ],
        "description": "MUArch\n======\n\n###### Continuous Integration\n\n[![Build Status](https://travis-ci.com/DanielBok/muarch.svg?branch=master)](https://travis-ci.com/DanielBok/muarch)\n[![Build status](https://ci.appveyor.com/api/projects/status/i6wylcc8syvdbkih?svg=true)](https://ci.appveyor.com/project/DanielBok/muarch)\n\n###### Documentation\n\n[![Documentation Status](https://readthedocs.org/projects/muarch/badge/?version=latest)](https://muarch.readthedocs.io/en/latest/?badge=latest)\n\n###### Coverage\n\n[![Coverage Status](https://coveralls.io/repos/github/DanielBok/muarch/badge.svg?branch=master)](https://coveralls.io/github/DanielBok/muarch?branch=master)\n\n## Installing\n\nInstall and update using [pip](https://pip.pypa.io/en/stable/quickstart/) and on conda.\n\nThis is a wrapper on top of Kevin Sheppard's [ARCH](https://github.com/bashtage/arch) package. The purpose of which are to:  \n\n1. Enable faster Monte Carlo simulation\n2. Simulate innovations through copula marginals\n\nIn the package, there are 2 classes to aid you - `UArch` and `MUArch`. The `UArch` class can be defined using a similar API to `arch_model` in the original `arch` package. The `MUArch` is a collection of these `UArch` models. \n\nThus, if you have a function that generates uniform marginals, like a copula, you can create a dependence structure among the different marginals when simulating the GARCH processes.\n\nIf you need a copula package, I have one [here](https://github.com/DanielBok/copulae). :)\n\nExample\n-------\n\nI'll list out a simple procedure to do AR-GARCH-Copula simulations.\n\n```python\nfrom muarch import MUArch, UArch\nfrom muarch.datasets import load_etf\nfrom copulae import NormalCopula\n\n\nreturns = load_etf()  # load returns data\nnum_assets = returns.shape[1]\n\n# sets up a MUArch model collection where each model defaults to \n# mean: AR(1)\n# vol: GARCH(1, 1)\n# dist: normal \nmodels = MUArch(num_assets, mean='AR', lags=1) \n\n# set first model to AR(1)-GARCH(1, 1) with skewt innovations  \nmodels[0] = UArch('AR', lags=1, dist='skewt')  \n\n# fit model, if you get complaints regarding non-convergence, you can scale the data up \n# using the scale parameter in the UArch or MUArch. i.e. UArch(..., scale=100). This will\n# reduce numerical errors. Don't worry, I'll rescale the simulation values subsequently\nmodels.fit(returns)\n\n# Usually you'll want to fit the residuals to the copula, use the copula to generate the\n# residuals and subsequently transform it back to returns \n\nresiduals = models.residuals() # defaults to return the standardized residuals\n\n\ncop = NormalCopula(dim=num_assets) # use a normal copula, you could of course use a TCopula\ncop.fit(residuals)\n\n# simulate 10 steps into the future, over 4 repetitions. This will return a (10 x 4 x 3) array\nmodels.simulate_mc(10, 4, custom_dist=cop.random)\n```\n\nFuture Works\n------------\n\nThis is actually a temporary hack so that others can do GARCH copula simulation. Another issue is that an ARFIMA mean model is not so easily specified (and simulated from) with the original `arch` package. You could specify an ARFIMA (or even just an ARMA model for the matter), fit it separately then use the residuals to fit a zero-mean model (pure GARCH). However, in such a way, the simulation is not so straightforward as you'll have to stitch the simulations from GARCH process and the mean model process back.\n\n\n",
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