Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk
Ano de defesa: | 2019 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Ciências Sociais e Humanas |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Administração
|
Departamento: |
Administração
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/17142 |
Resumo: | This research proposes a comparative analysis of some conditional volatility models for the calculation of Value-at-Risk (VaR) applied to the main financial series of the crypto-currencies market. Conditional volatility models of the ARCH family were used, taking into account markov-switching changes. Specifically, we used the EGARCH and MS-EGARCH models estimated from four different distributions, Normal, Normal Asymmetric, Student-t and Student-t Asymmetric, to model and make predictions for the time series of Bitcoin, Bitcoin Cash, Ripple, Ethereum, EOS and Stellar. Estimates confirm the existence of two states: the first regime is characterized greater volatility and less affected by asymmetries, while the second reveals greater effect of the arrival of information, ie is more sensitive to asymmetric shock and less persistence of volatility. To complement the analysis of the volatility models, risk estimates were generated from Value-at-Risk. Thus, we performed the process to obtain the estimates of the VaR estimates for 100 steps forward with readjustment of the parameters at each step obtained for α = 1% and α = 5%. It should be noted that MS-EGARCH exceeded EGARCH-type models by 1%, indicating that this model is the most appropriate for estimation of the value at risk in the extreme quantile of 1%, that is, the model with change of Markovian regime made a prediction closer to perfection. However, in 5% the occurrence of losses was similar between the models. In this case, regardless of the number of regimes, there was an overestimation of VaR, that is, there were violations between expected and expected losses. As there were no statistically robust results, there is no way to imply that the MS-EGARCH model exceeds in large magnitudes the EGARCH model in a single prediction of 100 steps forward. Instead, it can be inferred that models with regime change can more accurately accommodate the properties of the financial returns and dynamics present in their volatility. |
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2019-06-25T21:20:55Z2019-06-25T21:20:55Z2019-02-19http://repositorio.ufsm.br/handle/1/17142This research proposes a comparative analysis of some conditional volatility models for the calculation of Value-at-Risk (VaR) applied to the main financial series of the crypto-currencies market. Conditional volatility models of the ARCH family were used, taking into account markov-switching changes. Specifically, we used the EGARCH and MS-EGARCH models estimated from four different distributions, Normal, Normal Asymmetric, Student-t and Student-t Asymmetric, to model and make predictions for the time series of Bitcoin, Bitcoin Cash, Ripple, Ethereum, EOS and Stellar. Estimates confirm the existence of two states: the first regime is characterized greater volatility and less affected by asymmetries, while the second reveals greater effect of the arrival of information, ie is more sensitive to asymmetric shock and less persistence of volatility. To complement the analysis of the volatility models, risk estimates were generated from Value-at-Risk. Thus, we performed the process to obtain the estimates of the VaR estimates for 100 steps forward with readjustment of the parameters at each step obtained for α = 1% and α = 5%. It should be noted that MS-EGARCH exceeded EGARCH-type models by 1%, indicating that this model is the most appropriate for estimation of the value at risk in the extreme quantile of 1%, that is, the model with change of Markovian regime made a prediction closer to perfection. However, in 5% the occurrence of losses was similar between the models. In this case, regardless of the number of regimes, there was an overestimation of VaR, that is, there were violations between expected and expected losses. As there were no statistically robust results, there is no way to imply that the MS-EGARCH model exceeds in large magnitudes the EGARCH model in a single prediction of 100 steps forward. Instead, it can be inferred that models with regime change can more accurately accommodate the properties of the financial returns and dynamics present in their volatility.Esta pesquisa propõe uma análise comparativa de alguns modelos de volatilidade condicional para o cálculo do Value-at-Risk (VaR) aplicado as principais séries financeiras do mercado de criptomoedas. Foram utilizados modelos de volatilidade condicional da família ARCH levando em consideração mudanças de regime markoviano. Em específico, utilizaram-se os modelos EGARCH e MS-EGARCH estimados a partir de quatro diferentes distribuições, Normal, Normal Assimétrica, Student-t e Student-t Assimétrica, para modelar e fazer previsões para as séries do Bitcoin, Bitcoin Cash, Ripple, Ethereum, EOS e Stellar. As estimativas confirmam a existência de dois estados: o primeiro regime é caracterizado maior volatilidade e menos afetado por assimetrias, enquanto o segundo revela ser mais sensível à choque assimétricos e têm menor persistência da volatilidade. Para complementar a análise dos modelos de volatilidade foram geradas estimativas de risco a partir do Value-at-Risk. Dessa forma, foi realizado o processo para obter as previsões das estimativas do VaR para 100 passos à frente com reajuste dos parâmetros a cada passo obtidas para α = 1% e α = 5%. Pode-se notar que o MS-EGARCH superou os modelos do tipo EGARCH em 1%, indicando que este modelo é o mais adequado para estimação do valor em risco no quantil extremo de 1%, ou seja, o modelo com mudança de regime markoviano realizou uma previsão mais próxima da perfeição. Todavia, em 5% a ocorrência de perdas foi similar entre os modelos. Neste caso, independente do número de regimes houve uma superestimação do VaR, ou seja, ocorreram violações entre as perdas esperadas e previstas. Como não houve resultados estatisticamente robustos, não há como implicar que o modelo MS-EGARCH supera em grandes magnitudes o modelo EGARCH em uma previsão única de 100 passos à frente. Ao invés disso, pode-se inferir os modelos com mudança de regime conseguem acomodar com mais precisão às propriedades dos retornos financeiros e das dinâmicas presentes em sua volatilidade.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de Ciências Sociais e HumanasPrograma de Pós-Graduação em AdministraçãoUFSMBrasilAdministraçãoAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessPrevisãoCriptomoedasValue-at-RiskForecastingCryptocurrencyCNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAOMudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-riskMarkov-switching change in the volatility dynamics of the cryptocurrencies market and its reflections in the value-at-risk forecastinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisCeretta, Paulo Sergiohttp://lattes.cnpq.br/3049029014914257Souza, Adriano Mendonçahttp://lattes.cnpq.br/5271075797851198Milani, Brunohttp://lattes.cnpq.br/0005005751598450http://lattes.cnpq.br/1245982332405570Marschner, Paulo Fernando600200000006600594dad0f-39e8-46c0-9299-beeff34883431e4db49c-9268-425d-81e5-517c8847daec6d3ff5eb-4c65-4863-bc39-5de8ce2a29e9e725ff00-5df9-4f8a-a68c-28fe48abdbcbreponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk |
dc.title.alternative.eng.fl_str_mv |
Markov-switching change in the volatility dynamics of the cryptocurrencies market and its reflections in the value-at-risk forecasting |
title |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk |
spellingShingle |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk Marschner, Paulo Fernando Previsão Criptomoedas Value-at-Risk Forecasting Cryptocurrency CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
title_short |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk |
title_full |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk |
title_fullStr |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk |
title_full_unstemmed |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk |
title_sort |
Mudança de regime markoviano na dinâmica de volatilidade do mercado de criptomoedas e seus reflexos na previsão do value-at-risk |
author |
Marschner, Paulo Fernando |
author_facet |
Marschner, Paulo Fernando |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Ceretta, Paulo Sergio |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/3049029014914257 |
dc.contributor.referee1.fl_str_mv |
Souza, Adriano Mendonça |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/5271075797851198 |
dc.contributor.referee2.fl_str_mv |
Milani, Bruno |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/0005005751598450 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1245982332405570 |
dc.contributor.author.fl_str_mv |
Marschner, Paulo Fernando |
contributor_str_mv |
Ceretta, Paulo Sergio Souza, Adriano Mendonça Milani, Bruno |
dc.subject.por.fl_str_mv |
Previsão Criptomoedas |
topic |
Previsão Criptomoedas Value-at-Risk Forecasting Cryptocurrency CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
dc.subject.eng.fl_str_mv |
Value-at-Risk Forecasting Cryptocurrency |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
description |
This research proposes a comparative analysis of some conditional volatility models for the calculation of Value-at-Risk (VaR) applied to the main financial series of the crypto-currencies market. Conditional volatility models of the ARCH family were used, taking into account markov-switching changes. Specifically, we used the EGARCH and MS-EGARCH models estimated from four different distributions, Normal, Normal Asymmetric, Student-t and Student-t Asymmetric, to model and make predictions for the time series of Bitcoin, Bitcoin Cash, Ripple, Ethereum, EOS and Stellar. Estimates confirm the existence of two states: the first regime is characterized greater volatility and less affected by asymmetries, while the second reveals greater effect of the arrival of information, ie is more sensitive to asymmetric shock and less persistence of volatility. To complement the analysis of the volatility models, risk estimates were generated from Value-at-Risk. Thus, we performed the process to obtain the estimates of the VaR estimates for 100 steps forward with readjustment of the parameters at each step obtained for α = 1% and α = 5%. It should be noted that MS-EGARCH exceeded EGARCH-type models by 1%, indicating that this model is the most appropriate for estimation of the value at risk in the extreme quantile of 1%, that is, the model with change of Markovian regime made a prediction closer to perfection. However, in 5% the occurrence of losses was similar between the models. In this case, regardless of the number of regimes, there was an overestimation of VaR, that is, there were violations between expected and expected losses. As there were no statistically robust results, there is no way to imply that the MS-EGARCH model exceeds in large magnitudes the EGARCH model in a single prediction of 100 steps forward. Instead, it can be inferred that models with regime change can more accurately accommodate the properties of the financial returns and dynamics present in their volatility. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-06-25T21:20:55Z |
dc.date.available.fl_str_mv |
2019-06-25T21:20:55Z |
dc.date.issued.fl_str_mv |
2019-02-19 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/17142 |
url |
http://repositorio.ufsm.br/handle/1/17142 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
600200000006 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
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dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Sociais e Humanas |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Administração |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Administração |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Ciências Sociais e Humanas |
dc.source.none.fl_str_mv |
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