<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>Replication Data for: A GARCH Tutorial with R</titl><IDNo agency="DOI">doi:10.7910/DVN/C4WHUJ</IDNo></titlStmt><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><distDate>2020-07-09</distDate></distStmt><verStmt source="archive"><version date="2020-07-09" type="RELEASED">1</version></verStmt><biblCit>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</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>Replication Data for: A GARCH Tutorial with R</titl><IDNo agency="DOI">doi:10.7910/DVN/C4WHUJ</IDNo></titlStmt><rspStmt><AuthEnty affiliation="Universidade Federal do Rio Grande do Sul">Marcelo Perlin</AuthEnty><AuthEnty affiliation="Universidade Federal de Ciências da Saúde de Porto Alegre">Mauro Mastella</AuthEnty><AuthEnty affiliation="Universidade do Vale do Rio dos Sinos">Daniel Vancin</AuthEnty><AuthEnty affiliation="Universidade Federal do Rio Grande do Sul">Henrique Ramos</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><contact affiliation="Universidade Federal do Rio Grande do Sul" email="marcelo.perlin@ufrgs.br">Marcelo Perlin</contact><depositr>RAC - Journal of Contemporary Administration</depositr><depDate>2020-07-08</depDate></distStmt><holdings URI="https://doi.org/10.7910/DVN/C4WHUJ"/></citation><stdyInfo><subject><keyword xml:lang="en">Business and Management</keyword><keyword>GARCH</keyword><keyword>Tutorial-article</keyword></subject><abstract date="2020-07-08">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.</abstract><sumDscr/></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt/><notes type="DVN:TOU" level="dv">&lt;a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0&lt;/a></notes></dataAccs><othrStdyMat><relPubl><citation><titlStmt><titl>Perlin, M. S., Mastella, M., Vancin, D. F., &amp; Ramos, H. P. (2021). A GARCH tutorial with R. Journal of Contemporary Administration, 25(1), e200088. https://doi.org/10.1590/1982-7849rac2021200088</titl></titlStmt><biblCit>Perlin, M. S., Mastella, M., Vancin, D. F., &amp; Ramos, H. P. (2021). A GARCH tutorial with R. 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