Markov-Switching GARCH Models in R: The MSGARCH Package

Ardia, David; Bluteau, Keven; Boudt, Kris; Catania, Leopoldo; Trottier, Denis Alexandre

doi:10.18637/jss.v091.i04

Cited by 76 publications

(37 citation statements)

References 44 publications

(69 reference statements)

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“…The GARCH model introduced by (Bollerslev, 1986) is a model with fixed parameters, which implies that the persistence of the conditional variance is constant over time. A model that changes the persistence of such conditional volatility is given by a Markov process with discrete dynamics, such as the MS-GARCH model, which varies the parameters over time for a latent discrete Markovian process (Ardia, Bluteau, Boudt, Catania, & Trottier, 2019).…”

Section: Methodsmentioning

confidence: 99%

A Bayesian study of changes in volatility of Bitcoin

Rojas

Coronado²

2019

CyA

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This paper is aimed at studying a MS-GARCH model applied to Bitcoin. The Bayesian estimation of the model shows that Bitcoin’s volatility can be modelled using two states of volatility, high and low. The modelled volatility is not stable over time. Twenty eight periods of high volatility were found, the largest period of volatility occurred during 2013. The findings help explain what happened during these high volatility periods.

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Section: Methodsmentioning

confidence: 99%

A Bayesian study of changes in volatility of Bitcoin

Rojas

Coronado²

2019

CyA

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“…The estimation of advanced GARCH specifications, such as regime switching volatility models, is available in R, but not used in this tutorial. As a reference, see the work of Ardia, Bluteau, Boudt, Catania and Trottier (2019).…”

Section: Application Of a Garch Model Application Of A Garch Modelmentioning

confidence: 99%

A GARCH Tutorial with R

et al. 2021

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Context: modeling volatility is an advanced technique in financial econometrics, with several applications for academic research. Objective: in this tutorial paper, we will address the topic of volatility modeling in R. We will discuss the underlying logic of GARCH models, their representation and estimation process, along with a descriptive example of a real-world application of volatility modeling. Methods: we use a GARCH model to predict how much time it will take, after the latest crisis, for the Ibovespa index to reach its historical peak once again. The empirical data covers the period between years 2000 and 2020, including the 2009 financial crisis and the current 2020’s episode of the COVID-19 pandemic. Conclusion: we find that, according to our GARCH model, Ibovespa is more likely than not to reach its peak once again in one year and four months from June 2020. All data and R code used to produce this tutorial are freely available on the internet and all results can be easily replicated.

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“…The series covers the period from January 2003 to June 2020. Various routines are done in R 3.6.3 with included packages such as MS-GARCH of Ardia et al (2019) and the 'pcadapt' package of Luu et al (2017).…”

Section: Data and Materialsmentioning

confidence: 99%

“…An essential point is that the conditional variance depends on only the small number of regimes that can be included in the model. Ardia et al (2019) derived a third specification. These authors argue that the path dependency issue is often resolved by not neglecting the necessary persistence effects of the second GARCH term.…”

Section: Markov-switching Generalized Autoregressive Conditional Hetementioning

confidence: 99%

Identifying structural changes in the exchange rates of South Africa as a regime-switching process

Makatjane¹,

Wyk²

2020

WIDER Working Paper

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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

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Markov-Switching GARCH Models in R: The MSGARCH Package

Cited by 76 publications

References 44 publications

A Bayesian study of changes in volatility of Bitcoin

A Bayesian study of changes in volatility of Bitcoin

A GARCH Tutorial with R

Identifying structural changes in the exchange rates of South Africa as a regime-switching process

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