Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações do UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/4791 |
Resumo: | In an increasingly competitive economic environment, as in the current global context, risk management becomes essential for the survival of companies and investment portfolio managers. Both companies and managers need to have a model that can be able to quantify the risks inherent in their investments in the best possible way in order to guide them in making decisions to get the highest expected return on their investments. Currently, there are several heterogeneous models which seek to quantify risk, making the choice of a particular model very complex. In order to confront and find models that can serve, efficiently, to the quantification of risk, the objective of this research is to compare the predictive ability of five models of conditional heteroskedasticity by estimating the Value-at-Risk, assuming eight different statistical probability distributions, for the series of financial ratios of the capital market of the five largest emerging countries: Brazil, Russia, India, China and South Africa, in the period between February 26, 2001 and December 31, 2015. For this goal was achieved, were held predictions of Value-at-Risk for 50 steps ahead, for all competing models in the study, with adjustment of parameters at every step. Since all the forecasts have been computed for every steps forward, it was possible to compare predictive ability of competing models studied by means of some loss functions. The evidences suggests that heterocedastic Component GARCH is preferable, to make predictions of Value-at-Risk, to all other competing models, however the distribution of statistical probability that this model uses interferes too much in the results of forecasts obtained by it. The data for each financial index studied showed to adapt themselves to a particular different type of probability density function, not reflecting a distribution which can be considered superior to all other. Thus, the results do not provide a single and ideal tool for use in the risk measurement, of generalized form, for all capital markets of emerging countries studied, only provide specific tools to be used in each financial index individually. The results found can be used for the purposes previously described or to elaborate statistical formulas that combine different models estimated in order to get better volatilities forecast measures so that it can measure, more precisely, the market risks. |
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2017-04-242017-04-242016-07-22AMARO, Raphael Silveira. Predictive ability comparison of heteroskedastic models by estimating the value-at-risk. 2016. 102 f. Dissertação (Mestrado em Administração) - Universidade Federal de Santa Maria, Santa Maria, 2016.http://repositorio.ufsm.br/handle/1/4791In an increasingly competitive economic environment, as in the current global context, risk management becomes essential for the survival of companies and investment portfolio managers. Both companies and managers need to have a model that can be able to quantify the risks inherent in their investments in the best possible way in order to guide them in making decisions to get the highest expected return on their investments. Currently, there are several heterogeneous models which seek to quantify risk, making the choice of a particular model very complex. In order to confront and find models that can serve, efficiently, to the quantification of risk, the objective of this research is to compare the predictive ability of five models of conditional heteroskedasticity by estimating the Value-at-Risk, assuming eight different statistical probability distributions, for the series of financial ratios of the capital market of the five largest emerging countries: Brazil, Russia, India, China and South Africa, in the period between February 26, 2001 and December 31, 2015. For this goal was achieved, were held predictions of Value-at-Risk for 50 steps ahead, for all competing models in the study, with adjustment of parameters at every step. Since all the forecasts have been computed for every steps forward, it was possible to compare predictive ability of competing models studied by means of some loss functions. The evidences suggests that heterocedastic Component GARCH is preferable, to make predictions of Value-at-Risk, to all other competing models, however the distribution of statistical probability that this model uses interferes too much in the results of forecasts obtained by it. The data for each financial index studied showed to adapt themselves to a particular different type of probability density function, not reflecting a distribution which can be considered superior to all other. Thus, the results do not provide a single and ideal tool for use in the risk measurement, of generalized form, for all capital markets of emerging countries studied, only provide specific tools to be used in each financial index individually. The results found can be used for the purposes previously described or to elaborate statistical formulas that combine different models estimated in order to get better volatilities forecast measures so that it can measure, more precisely, the market risks.Em um ambiente econômico cada vez mais competitivo, como é no atual contexto mundial, a gestão de risco torna-se indispensável para a sobrevivência de empresas e de gestores de carteiras de investimento. Tanto as empresas quanto os gestores precisam de um modelo que seja capaz de quantificar os riscos inerentes aos seus investimentos financeiros da melhor maneira possível, de forma a orientá-los na tomada de decisões para que obtenham o maior retorno esperado de seus investimentos. Atualmente, existem inúmeros modelos heterogêneos que buscam quantificar riscos, tornando a escolha de um determinado modelo bastante complexa. Com o intuito de confrontar e encontrar modelos que possam servir, de forma eficiente, à quantificação de riscos, o objetivo desta pesquisa é o de comparar a capacidade preditiva de cinco modelos de heterocedasticidade condicional através da estimação do Value-at-Risk, levando em consideração oito distribuições de probabilidade estatística diferentes, para as séries de índices financeiros do mercado de capitais dos cinco maiores países emergentes: Brasil, Rússia, Índia, China e África do Sul, no período compreendido entre 26 de fevereiro de 2001 e 31 de dezembro de 2015. Para alcançar tal objetivo, realizaram-se previsões do Value-at-Risk para 50 passos à frente, em todos os modelos concorrentes em estudo, com reajuste dos parâmetros a cada passo. Uma vez que todas as previsões foram computadas para todos os passos à frente, foi possível realizar a comparação da capacidade preditiva dos modelos concorrentes estudados por meio de determinadas funções de perda específicas. As evidências encontradas sugerem que o modelo heterocedástico Component GARCH é preferível, para realizar previsões do Value-at-Risk, a todos os outros modelos concorrentes, porém a distribuição de probabilidade estatística que este modelo utiliza interfere demasiadamente nos resultados das previsões obtidas por ele. Os dados de cada índice financeiro estudado mostraram-se adequar-se a um determinado tipo de função de densidade de probabilidade diferente, não refletindo uma distribuição que possa ser considerada superior a todas as outras. Deste modo, os resultados encontrados não oferecem uma ferramenta única e ideal para ser utilizada na mensuração de risco, de forma generalizada, para todos os mercados de capitais dos países emergentes estudados, apenas fornecem ferramentas pontuais para serem utilizadas em cada índice financeiro de forma individual. Os resultados obtidos podem servir para os fins descritos anteriormente ou para elaborar fórmulas estatísticas que combinem diferentes modelos estimados com a finalidade de obter melhores medidas de previsão de volatilidades para que se possa mensurar, de forma mais precisa, os riscos de mercado.application/pdfporUniversidade Federal de Santa MariaPrograma de Pós-Graduação em AdministraçãoUFSMBRAdministraçãoValue-at-riskModelos heterocedásticosDistribuições de probabilidadeCapacidade preditivaValue-at-riskHeteroskedastic modelsProbability distributionsPredictive abilityCNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAOComparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-riskPredictive ability comparison of heteroskedastic models by estimating the value-at-riskinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisCeretta, Paulo Sergiohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4707322J1Sonza, Igor Bernardihttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4745888D9Milani, Brunohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4481252Y8http://lattes.cnpq.br/8621904059935562Amaro, Raphael Silveira600200000006400500300500300594dad0f-39e8-46c0-9299-beeff3488343d1eccfa1-b261-419f-80e5-b9d8fd518ed35e29aeea-5765-4693-8c0b-8c18d2b66366e725ff00-5df9-4f8a-a68c-28fe48abdbcbinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALAMARO, RAPHAEL SILVEIRA.pdfapplication/pdf2162569http://repositorio.ufsm.br/bitstream/1/4791/1/AMARO%2c%20RAPHAEL%20SILVEIRA.pdfe7ed2bcda88f19001ee45e104583bcb2MD51TEXTAMARO, RAPHAEL SILVEIRA.pdf.txtAMARO, RAPHAEL SILVEIRA.pdf.txtExtracted texttext/plain223598http://repositorio.ufsm.br/bitstream/1/4791/2/AMARO%2c%20RAPHAEL%20SILVEIRA.pdf.txt5a093b7f37f5d343275f9d22050bd8ddMD52THUMBNAILAMARO, RAPHAEL SILVEIRA.pdf.jpgAMARO, RAPHAEL SILVEIRA.pdf.jpgIM Thumbnailimage/jpeg4244http://repositorio.ufsm.br/bitstream/1/4791/3/AMARO%2c%20RAPHAEL%20SILVEIRA.pdf.jpge8a9906a98298f7cffd140365e0414adMD531/47912017-07-25 11:10:02.779oai:repositorio.ufsm.br:1/4791Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2017-07-25T14:10:02Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk |
dc.title.alternative.eng.fl_str_mv |
Predictive ability comparison of heteroskedastic models by estimating the value-at-risk |
title |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk |
spellingShingle |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk Amaro, Raphael Silveira Value-at-risk Modelos heterocedásticos Distribuições de probabilidade Capacidade preditiva Value-at-risk Heteroskedastic models Probability distributions Predictive ability CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
title_short |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk |
title_full |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk |
title_fullStr |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk |
title_full_unstemmed |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk |
title_sort |
Comparação da capacidade preditiva de modelos heterocedásticos através da estimação do value-at-risk |
author |
Amaro, Raphael Silveira |
author_facet |
Amaro, Raphael Silveira |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Ceretta, Paulo Sergio |
dc.contributor.advisor1Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4707322J1 |
dc.contributor.referee1.fl_str_mv |
Sonza, Igor Bernardi |
dc.contributor.referee1Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4745888D9 |
dc.contributor.referee2.fl_str_mv |
Milani, Bruno |
dc.contributor.referee2Lattes.fl_str_mv |
http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4481252Y8 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8621904059935562 |
dc.contributor.author.fl_str_mv |
Amaro, Raphael Silveira |
contributor_str_mv |
Ceretta, Paulo Sergio Sonza, Igor Bernardi Milani, Bruno |
dc.subject.por.fl_str_mv |
Value-at-risk Modelos heterocedásticos Distribuições de probabilidade Capacidade preditiva |
topic |
Value-at-risk Modelos heterocedásticos Distribuições de probabilidade Capacidade preditiva Value-at-risk Heteroskedastic models Probability distributions Predictive ability CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
dc.subject.eng.fl_str_mv |
Value-at-risk Heteroskedastic models Probability distributions Predictive ability |
dc.subject.cnpq.fl_str_mv |
CNPQ::CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO |
description |
In an increasingly competitive economic environment, as in the current global context, risk management becomes essential for the survival of companies and investment portfolio managers. Both companies and managers need to have a model that can be able to quantify the risks inherent in their investments in the best possible way in order to guide them in making decisions to get the highest expected return on their investments. Currently, there are several heterogeneous models which seek to quantify risk, making the choice of a particular model very complex. In order to confront and find models that can serve, efficiently, to the quantification of risk, the objective of this research is to compare the predictive ability of five models of conditional heteroskedasticity by estimating the Value-at-Risk, assuming eight different statistical probability distributions, for the series of financial ratios of the capital market of the five largest emerging countries: Brazil, Russia, India, China and South Africa, in the period between February 26, 2001 and December 31, 2015. For this goal was achieved, were held predictions of Value-at-Risk for 50 steps ahead, for all competing models in the study, with adjustment of parameters at every step. Since all the forecasts have been computed for every steps forward, it was possible to compare predictive ability of competing models studied by means of some loss functions. The evidences suggests that heterocedastic Component GARCH is preferable, to make predictions of Value-at-Risk, to all other competing models, however the distribution of statistical probability that this model uses interferes too much in the results of forecasts obtained by it. The data for each financial index studied showed to adapt themselves to a particular different type of probability density function, not reflecting a distribution which can be considered superior to all other. Thus, the results do not provide a single and ideal tool for use in the risk measurement, of generalized form, for all capital markets of emerging countries studied, only provide specific tools to be used in each financial index individually. The results found can be used for the purposes previously described or to elaborate statistical formulas that combine different models estimated in order to get better volatilities forecast measures so that it can measure, more precisely, the market risks. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016-07-22 |
dc.date.accessioned.fl_str_mv |
2017-04-24 |
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2017-04-24 |
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masterThesis |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
AMARO, Raphael Silveira. Predictive ability comparison of heteroskedastic models by estimating the value-at-risk. 2016. 102 f. Dissertação (Mestrado em Administração) - Universidade Federal de Santa Maria, Santa Maria, 2016. |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/4791 |
identifier_str_mv |
AMARO, Raphael Silveira. Predictive ability comparison of heteroskedastic models by estimating the value-at-risk. 2016. 102 f. Dissertação (Mestrado em Administração) - Universidade Federal de Santa Maria, Santa Maria, 2016. |
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