10.7910/DVN/C4WHUJ
Marcelo Perlin
Marcelo
Perlin
https://orcid.org/0000-0002-9839-4268
Universidade Federal do Rio Grande do Sul
Mauro Mastella
Mauro
Mastella
https://orcid.org/0000-0002-7163-9448
Universidade Federal de Ciências da Saúde de Porto Alegre
Daniel Vancin
Daniel
Vancin
https://orcid.org/0000-0001-6303-0555
Universidade do Vale do Rio dos Sinos
Henrique Ramos
Henrique
Ramos
Universidade Federal do Rio Grande do Sul
Replication Data for: A GARCH Tutorial with R
Harvard Dataverse
2020
Business and Management
GARCH
Tutorial-article
Marcelo Perlin
Marcelo
Perlin
Universidade Federal do Rio Grande do Sul
2020-07-08
2020-07-09
10.7910/DVN/C4WHUJ/YNTGKT
10.7910/DVN/C4WHUJ/CLYL6B
10.7910/DVN/C4WHUJ/48A7TW
10.7910/DVN/C4WHUJ/M2ND6X
10.7910/DVN/C4WHUJ/ARTK64
10.7910/DVN/C4WHUJ/GLL5C7
10.7910/DVN/C4WHUJ/NJB4QG
10.7910/DVN/C4WHUJ/4URZRW
10.7910/DVN/C4WHUJ/PQKY5G
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2003
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text/markdown
type/x-r-syntax
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1.0
Creative Commons CC0 1.0 Universal Public Domain Dedication.
Context: Modelling 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 modelling. 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 pandemia. 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.