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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: A GARCH Tutorial with R |
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Identification Number: |
doi:10.7910/DVN/C4WHUJ |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2020-07-09 |
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Version: |
1 |
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Bibliographic Citation: |
Marcelo Perlin; Mauro Mastella; Daniel Vancin; Henrique Ramos, 2020, "Replication Data for: A GARCH Tutorial with R", https://doi.org/10.7910/DVN/C4WHUJ, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication Data for: A GARCH Tutorial with R |
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Identification Number: |
doi:10.7910/DVN/C4WHUJ |
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Authoring Entity: |
Marcelo Perlin (Universidade Federal do Rio Grande do Sul) |
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Mauro Mastella (Universidade Federal de Ciências da Saúde de Porto Alegre) |
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Daniel Vancin (Universidade do Vale do Rio dos Sinos) |
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Henrique Ramos (Universidade Federal do Rio Grande do Sul) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Marcelo Perlin |
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Depositor: |
RAC - Journal of Contemporary Administration |
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Date of Deposit: |
2020-07-08 |
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Holdings Information: |
https://doi.org/10.7910/DVN/C4WHUJ |
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Study Scope |
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Keywords: |
Business and Management, GARCH, Tutorial-article |
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Abstract: |
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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Perlin, M. S., Mastella, M., Vancin, D. F., & Ramos, H. P. (2021). A GARCH tutorial with R. Journal of Contemporary Administration, 25(1), e200088. https://doi.org/10.1590/1982-7849rac2021200088 |
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Bibliographic Citation: |
Perlin, M. S., Mastella, M., Vancin, D. F., & Ramos, H. P. (2021). A GARCH tutorial with R. Journal of Contemporary Administration, 25(1), e200088. https://doi.org/10.1590/1982-7849rac2021200088 |
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Label: |
00-Prepare_Computer.R |
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Notes: |
type/x-r-syntax |
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01-Get_Index_Data.R |
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Notes: |
type/x-r-syntax |
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02-Do_Descriptive_Figures.R |
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Notes: |
type/x-r-syntax |
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03-Do_ARCH_Test.R |
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Notes: |
type/x-r-syntax |
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04-Estimate_Garch_Model.R |
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Notes: |
type/x-r-syntax |
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05-Find_Best_Garch_Model.R |
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Notes: |
type/x-r-syntax |
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06-Simulate_Garch_Model.R |
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type/x-r-syntax |
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garch_fcts.R |
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Notes: |
type/x-r-syntax |
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Label: |
README.md |
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Notes: |
text/markdown |