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new readme and index files with updated references and lighter content

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ArdiaD committed Aug 27, 2018
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@@ -6,55 +6,7 @@ Markov-switching GARCH models in R
[![CRAN](http://www.r-pkg.org/badges/version/MSGARCH)](https://cran.r-project.org/package=MSGARCH) [![Downloads](http://cranlogs.r-pkg.org/badges/MSGARCH?color=brightgreen)](http://www.r-pkg.org/pkg/MSGARCH)[![Downloads](http://cranlogs.r-pkg.org/badges/grand-total/MSGARCH?color=brightgreen)](http://www.r-pkg.org/pkg/MSGARCH)
[![Pending Pull-Requests](http://githubbadges.herokuapp.com/keblu/MSGARCH/pulls.svg?style=flat)](https://github.com/keblu/MSGARCH/pulls)
[![Github Issues](http://githubbadges.herokuapp.com/keblu/MSGARCH/issues.svg)](https://github.com/keblu/MSGARCH/issues)
## Introduction
Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance dynamics of time series. The R package `MSGARCH` ([Ardia et al., 2017](https://ssrn.com/abstract=2845809), Ardia et al., 2018) implements Markov-switching GARCH-type models very effficiently by using C++ object-oriented programming techniques. It allows the user to perform simulations as well as Maximum Likelihood and MCMC/Bayesian estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, Value-at-Risk and Expected Shortfall are also available. See [Ardia et al. (2017)](https://ssrn.com/abstract=2845809) for further details. A large-scale empirical study is presented in [Ardia et al. (2017)](https://ssrn.com/abstract=2918413).
## Contents
* MSGARCH-manual.pdf: This document is the documentation for the MSGARCH package.
* Package: This folder contains the latest developpement version of the package.
* bin: This folder contains the previous stable version of the package.
## Installation
The latest stable version of `MSGARCH` is available on CRAN (https://CRAN.R-project.org/package=MSGARCH) and can be installed via:
R > install.packages("MSGARCH")
To install the latest development version of `MSGARCH` (which may contain bugs!) use these lines:
R > install.packages("devtools")
R > require("devtools")
R > devtools::install_github("keblu/MSGARCH", subdir="Package")
## Details
Major changes in the package make it such that code relying on the `MSGARCH` package previous to version 1.0 will not be compatible with the current and future version of the `MSGARCH` package. We however encourage the user to update the package to the latest version since many enhancement and bug fix has been implemented. Please refer to [Ardia et al., 2017](https://ssrn.com/abstract=2845809) for more information.
## References
Please cite `MSGARCH` in publications:
Ardia, D., Bluteau, K., Boudt, K., Catania, L., Trottier, D.-A. (2017).
_Markov-switching GARCH models in R: The MSGARCH package_.
Working paper, Forthcoming in _Journal of Statistical Software_.
https://ssrn.com/abstract=2845809
Ardia, D., Bluteau, K., Boudt, K., Catania, L. (2017).
_Forecasting risk with Markov-switching GARCH models: A large-scale performance study_.
_International Journal of Forecasting_, 34(4), 733-747.
https://doi.org/10.1016/j.ijforecast.2018.05.004
Ardia, D., Bluteau, K., Boudt, K., Catania, A. Ghalanos, L., Peterson, B., Trottier, D.-A. (2018).
_MSGARCH package_
Ardia, D., Bluteau, K., Ruede, M. (2018).
_Regime changes in Bitcoin GARCH volatility dynamics_.
Working paper, Forthcoming in _Finance Research Letters_.
http://dx.doi.org/10.2139/ssrn.3180830
## Acknowledgements
## Introduction
The MSGARCH team is grateful to Samuel Borms, Peter Carl, Yohan Chalabi, Dirk Eddelbuettel, Alexios Ghalanos,
Richard Gerlach, Laurent Fastnacht, Félix-Antoine Fortin, Lennart Hoogerheide, Rob J Hyndman, Eliane Maalouf, Brian Peterson, Tobias Setz, Enrico Schumann, Diethelm Wuertz, and participants at the R/Finance 2017 conference (Chicago), the 37th International Symposium on Forecasting (Cairns), UseR 2017 (Brussels), and Quant Insights 2017 (London). We acknowledge Industrielle-Alliance, International Institute of Forecasters, Google Summer of Code 2016 & 2017, FQRSC (Grant # 2015-NP-179931), and Fonds des Donations at the University of Neuchâtel for their financial support, and Calcul Quebec for computational support.
Check the `MSGARCH` webpage at http://keblu.github.io/MSGARCH/
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@@ -52,11 +52,9 @@ <h2 id="introduction">Introduction</h2>
<p align="justify">Markov-switching GARCH models have become popular to account for regime changes
in the conditional variance dynamics of financial time series.
The R package <code class="highlighter-rouge">MSGARCH</code> (<a href="https://ssrn.com/abstract=2845809">Ardia et al., 2017</a>)
The R package <code class="highlighter-rouge">MSGARCH</code> (<a href="https://ssrn.com/abstract=2845809">Ardia et al., 20xx</a>)
implements Markov-switching GARCH-type models very efficiently by using C++ object-oriented programming techniques.
It allows the user to perform simulations as well as Maximum Likelihood and MCMC/Bayesian estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, Value-at-Risk and Expected Shortfall are also available
See <a href="https://ssrn.com/abstract=2845809">Ardia et al. (2017)</a> for further details.
A large-scale empirical study is presented in <a href="https://ssrn.com/abstract=2918413">Ardia et al. (2017)</a>.</p>
It allows the user to perform simulations as well as Maximum Likelihood and MCMC/Bayesian estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, Value-at-Risk and Expected Shortfall are also available.</p>
<div style="text-align:center">
<iframe width="560" height="315" src="https://www.youtube.com/embed/h2pCpMYBIZQ" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>
@@ -80,29 +78,26 @@ <h2 id="references">References</h2>
<p>Please cite <code class="highlighter-rouge">MSGARCH</code> in publications:</p>
<p>Ardia, D., Bluteau, K., Boudt, K., Catania, L., Trottier, D.-A. (2017).<br />
<p>Ardia, D., Bluteau, K., Boudt, K., Catania, L., Trottier, D.-A. (20xx).<br />
<em>Markov-switching GARCH models in R: The MSGARCH package</em>.<br />
Working paper, Forthcoming in Journal of Statistical Software.<br />
<a href=https://ssrn.com/abstract=2845809>https://ssrn.com/abstract=2845809</a></p>
<p>Ardia, D., Bluteau, K., Boudt, K., Catania, L. (2017). <br />
<p>Ardia, D., Bluteau, K., Boudt, K., Catania, L. (2018a). <br />
<em>Forecasting risk with Markov-switching GARCH models: A large-scale performance study</em>. <br />
Working paper, Forthcoming in International Journal of Forecasting.<br />
<a href=https://ssrn.com/abstract=2918413>https://ssrn.com/abstract=2918413</a></p>
International Journal of Forecasting, Vol 34, Issue 4, pp. 733-747.<br />
<a href=https://doi.org/10.1016/j.ijforecast.2018.05.004>https://doi.org/10.1016/j.ijforecast.2018.05.004</a></p>
<p>Ardia, D., Bluteau, K., Boudt, Catania, L., Ghalanos, A., Peterson, B., Trottier, D.-A. (2018). <br />
<em>MSGARCH package</em>. <br />
<a href=https://CRAN.R-project.org/package=MSGARCH>https://CRAN.R-project.org/package=MSGARCH</a></p> </p>
<p>Ardia, D., Bluteau, K., Ruede, M. (2018). <br />
<p>Ardia, D., Bluteau, K., Ruede, M. (2018b). <br />
<em>Regime changes in Bitcoin GARCH volatility dynamics</em>. <br />
Working paper, Forthcoming in Finance Research Letters. <br />
<a href=https://ssrn.com/abstract=3180830>https://ssrn.com/abstract=3180830</a></p> </p>
Forthcoming in Finance Research Letters. <br />
<a href=https://doi.org/10.1016/j.frl.2018.08.009>https://doi.org/10.1016/j.frl.2018.08.009</a></p> </p>
<h2 id="acknowledgements">Acknowledgements</h2>
<p align="justify">The MSGARCH team is grateful to Samuel Borms, Peter Carl, Yohan Chalabi, Dirk Eddelbuettel, Alexios Ghalanos,
Richard Gerlach, Laurent Fastnacht, Félix-Antoine Fortin, Lennart Hoogerheide, Rob J Hyndman, Eliane Maalouf, Brian Peterson, Tobias Setz, Enrico Schumann, Diethelm Wuertz, and participants at the R/Finance 2017 conference (Chicago), the 37th International Symposium on Forecasting (Cairns), UseR 2017 (Brussels), and Quant Insights 2017 (London). We acknowledge Industrielle-Alliance, International Institute of Forecasters, Google Summer of Code 2016 & 2017, FQRSC (Grant # 2015-NP-179931), and Fonds des Donations at the University of Neuchâtel for their financial support, and Calcul Québec for computational support.</p>
<p align="justify">The MSGARCH core team is grateful to Samuel Borms, Peter Carl, Yohan Chalabi, Dirk Eddelbuettel, Alexios Ghalanos,
Richard Gerlach, Laurent Fastnacht, Félix-Antoine Fortin, Lennart Hoogerheide, Rob J Hyndman, Eliane Maalouf, Brian Peterson, Tobias Setz, Enrico Schumann, Diethelm Wuertz, and participants at the R/Finance 2017 conference (Chicago), the 37th International Symposium on Forecasting (Cairns), UseR 2017 (Brussels), Quant Insights 2017 (London), MAFE 2018 (Madrid), eRum 2018 (Budapest), and seminar participants at HEC Li`ege, Paris–Dauphine, and IAE–AMSE Aix–
Marseille. We acknowledge Industrielle-Alliance, International Institute of Forecasters, Google Summer of Code 2016 & 2017, FQRSC (Grant # 2015-NP-179931), and Fonds des Donations at the University of Neuchâtel for their financial support, and Calcul Québec for computational support.</p>
<h2 id="references">Core Team</h2>

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