Dynamic volatility models for market risk and portfolio analysis
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/34384 |
Resumo: | Dealing with financial time series brings many challenges to data modeling, given the existence of heavy tails and extreme return values caused by external events, such as politics, natural disasters, economical events, or even speculation. Providing reliable and interpretable insights to market agents, based on statistical models, to base strategic decisions on building assets portfolios, taking arbitrage decisions, and managing investment risks is crucial for avoiding losses and correctly pricing assets for developing successful investment strategies. Rego and Santos (2020) proposed the Non-Gaussian Stochastic Volatility Model with Jumps (NGSVJ) for market volatility evaluation, which includes automatic inference procedure that allows the model to be fast enough to bring tangible results for the user, using an ordinary home computer, to perform trading operations. The Dynamic Models (DM) class, on which the NGSVJ is based, has a flexible structure that enables the inclusion of new features on the models and has implementation simplicity from the computational perspective. The DM class of models is still unexplored for financial applications when compared to the other classes of models commonly used on literature, mainly based on Stochastic Volatility (SV) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) classes of models. In this thesis, several developments are made using as basis the DM class and the NGSVJ model. For dealing with a single asset, or univariate, financial time series, developments are made to the NGSVJ to be able to estimate the degree of freedom of the gamma mixture parameter and include a Hiden Markov (HMM) to give flexibility and interpretability to the model for applications on arbitrage intraday market operations. For dealing with multiple assets portfolio, or multivariate, financial time series the Multivariate Stochastic Volatility Model with Jumps (MSVJ) was developed, based on DM structure, to enable financial agents to estimate the volatility and correlation between portfolio assets and effectively develop a risk management strategy. This thesis provides a wide set of statistical models, based on DM class, that can be used in finance for taking arbitrage and investment decisions, whether it is used for analyzing a single asset or a portfolio. Simulation studies are presented as well as applications on the S\&P 500 market index, commodity derivatives, and exchange rates, to illustrate model performance. The proposed models have highly interpretable results, bringing major developments to the DM class of models and their applications on finance. The proposed models are robust in the sense to incorporate several stylized characteristics of return data, bringing major developments to the NGSVJ and their applications. |
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Thiago Rezende Dos Santoshttp://lattes.cnpq.br/9458275921031976Fábio Nogueira DemarquiCristiano de Carvalho SantosFernando Ferraz do NascimentoJoão Batista de Morais Pereirahttp://lattes.cnpq.br/4493838388491868Arthur Tarso Rego2020-11-17T13:19:08Z2020-11-17T13:19:08Z2020-08-28http://hdl.handle.net/1843/34384Dealing with financial time series brings many challenges to data modeling, given the existence of heavy tails and extreme return values caused by external events, such as politics, natural disasters, economical events, or even speculation. Providing reliable and interpretable insights to market agents, based on statistical models, to base strategic decisions on building assets portfolios, taking arbitrage decisions, and managing investment risks is crucial for avoiding losses and correctly pricing assets for developing successful investment strategies. Rego and Santos (2020) proposed the Non-Gaussian Stochastic Volatility Model with Jumps (NGSVJ) for market volatility evaluation, which includes automatic inference procedure that allows the model to be fast enough to bring tangible results for the user, using an ordinary home computer, to perform trading operations. The Dynamic Models (DM) class, on which the NGSVJ is based, has a flexible structure that enables the inclusion of new features on the models and has implementation simplicity from the computational perspective. The DM class of models is still unexplored for financial applications when compared to the other classes of models commonly used on literature, mainly based on Stochastic Volatility (SV) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) classes of models. In this thesis, several developments are made using as basis the DM class and the NGSVJ model. For dealing with a single asset, or univariate, financial time series, developments are made to the NGSVJ to be able to estimate the degree of freedom of the gamma mixture parameter and include a Hiden Markov (HMM) to give flexibility and interpretability to the model for applications on arbitrage intraday market operations. For dealing with multiple assets portfolio, or multivariate, financial time series the Multivariate Stochastic Volatility Model with Jumps (MSVJ) was developed, based on DM structure, to enable financial agents to estimate the volatility and correlation between portfolio assets and effectively develop a risk management strategy. This thesis provides a wide set of statistical models, based on DM class, that can be used in finance for taking arbitrage and investment decisions, whether it is used for analyzing a single asset or a portfolio. Simulation studies are presented as well as applications on the S\&P 500 market index, commodity derivatives, and exchange rates, to illustrate model performance. The proposed models have highly interpretable results, bringing major developments to the DM class of models and their applications on finance. The proposed models are robust in the sense to incorporate several stylized characteristics of return data, bringing major developments to the NGSVJ and their applications.Lidar com séries temporais financeiras traz muitos desafios à modelagem de dados, dada a existência de caudas pesadas e valores extremos de retorno causados por eventos externos, como eventos políticos, econômicos, desastres naturais ou por especulação. Fornecer insights confiáveis e interpretáveis aos agentes do mercado, com base em modelos estatísticos, para basear decisões estratégicas na criação de portfólios de ativos, tomar decisões de arbitragem e gerenciar riscos de investimento é crucial para evitar perdas e precificar corretamente os ativos para o desenvolvimento de estratégias de investimento bem sucedidas. Rego e Santos (2020) propuseram o Modelo Não-Gaussiano de Volatilidade Estocástica com Saltos (NGSVJ) para avaliação da volatilidade do mercado, que inclui procedimento de inferência automática, permitindo que o modelo seja rápido o suficiente para trazer resultados tangíveis para o usuário executar operações de negociação. A classe de Modelos Dinâmicos (DM), da qual o NGSVJ faz parte, possui uma estrutura flexível que permite a inclusão de novos recursos nos modelos e possui simplicidade de implementação pela ótica computacional. Essa classe de modelos ainda é inexplorada para aplicações financeiras quando comparada às outras classes de modelos comumente usadas na literatura, principalmente baseadas nas classes de Volatilidade Estocástica (SV) e Heterocedasticidade Condicional Autorregressiva Generalizada (GARCH). Nesta tese, vários desenvolvimentos são feitos usando como base a classe DM e o modelo NGSVJ. No âmbito de séries temporais financeiras univariadas, desenvolvimentos são feitos ao NGSVJ para estimar o grau de liberdade do parâmetro da mistura Gamma e incluir uma estrutura de Markov Oculto (HMM) para fornecer ao modelo flexibilidade e interpretabilidade em operações de mercado intraday de arbitragem. No âmbito de séries temporais financeiras multivariadas, o Modelo Multivariado de Volatilidade Estocástica com Saltos (MSVJ) foi desenvolvido para permitir aos agentes financeiros estimar a volatilidade e correlação entre os ativos do portfólio simultaneamente e desenvolver estratégias efetivas de gerenciamento de riscos. Esta tese fornece um conjunto amplo de modelos estatísticos, baseados na classe DM, que podem ser usados em finanças para tomar decisões de arbitragem e investimento, sejam para análise de um único ativo ou portfólio. São apresentados estudos de simulação, bem como aplicações no índice de mercado S&P 500, derivativos de commodities e taxas de câmbio, para ilustrar o desempenho do modelo. Os modelos propostos têm resultados altamente interpretáveis, trazendo grandes desenvolvimentos para a classe de modelos DM e suas aplicações em finanças.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorengUniversidade Federal de Minas GeraisPrograma de Pós-Graduação em EstatísticaUFMGBrasilhttp://creativecommons.org/licenses/by-nc-nd/3.0/pt/info:eu-repo/semantics/openAccessEstatística – Teses.Analise de séries temporais – Teses.Processo estocástico – Teses.Modelo dinâmico – Teses.Análise multivariada - Teses.Financial Time SeriesDynamic ModelsMultivariate Stochastic ModelsDynamic volatility models for market risk and portfolio analysisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALTese_Arthur_Tarso_Rego_Final.pdfTese_Arthur_Tarso_Rego_Final.pdfTese Doutorado Arthur Tarso Regoapplication/pdf9271128https://repositorio.ufmg.br/bitstream/1843/34384/1/Tese_Arthur_Tarso_Rego_Final.pdfcf171baddbc847d579ac49a5746bde59MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufmg.br/bitstream/1843/34384/2/license_rdfcfd6801dba008cb6adbd9838b81582abMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82119https://repositorio.ufmg.br/bitstream/1843/34384/3/license.txt34badce4be7e31e3adb4575ae96af679MD531843/343842020-11-17 10:19:08.546oai:repositorio.ufmg.br:1843/34384TElDRU7Dh0EgREUgRElTVFJJQlVJw4fDg08gTsODTy1FWENMVVNJVkEgRE8gUkVQT1NJVMOTUklPIElOU1RJVFVDSU9OQUwgREEgVUZNRwoKQ29tIGEgYXByZXNlbnRhw6fDo28gZGVzdGEgbGljZW7Dp2EsIHZvY8OqIChvIGF1dG9yIChlcykgb3UgbyB0aXR1bGFyIGRvcyBkaXJlaXRvcyBkZSBhdXRvcikgY29uY2VkZSBhbyBSZXBvc2l0w7NyaW8gSW5zdGl0dWNpb25hbCBkYSBVRk1HIChSSS1VRk1HKSBvIGRpcmVpdG8gbsOjbyBleGNsdXNpdm8gZSBpcnJldm9nw6F2ZWwgZGUgcmVwcm9kdXppciBlL291IGRpc3RyaWJ1aXIgYSBzdWEgcHVibGljYcOnw6NvIChpbmNsdWluZG8gbyByZXN1bW8pIHBvciB0b2RvIG8gbXVuZG8gbm8gZm9ybWF0byBpbXByZXNzbyBlIGVsZXRyw7RuaWNvIGUgZW0gcXVhbHF1ZXIgbWVpbywgaW5jbHVpbmRvIG9zIGZvcm1hdG9zIMOhdWRpbyBvdSB2w61kZW8uCgpWb2PDqiBkZWNsYXJhIHF1ZSBjb25oZWNlIGEgcG9sw610aWNhIGRlIGNvcHlyaWdodCBkYSBlZGl0b3JhIGRvIHNldSBkb2N1bWVudG8gZSBxdWUgY29uaGVjZSBlIGFjZWl0YSBhcyBEaXJldHJpemVzIGRvIFJJLVVGTUcuCgpWb2PDqiBjb25jb3JkYSBxdWUgbyBSZXBvc2l0w7NyaW8gSW5zdGl0dWNpb25hbCBkYSBVRk1HIHBvZGUsIHNlbSBhbHRlcmFyIG8gY29udGXDumRvLCB0cmFuc3BvciBhIHN1YSBwdWJsaWNhw6fDo28gcGFyYSBxdWFscXVlciBtZWlvIG91IGZvcm1hdG8gcGFyYSBmaW5zIGRlIHByZXNlcnZhw6fDo28uCgpWb2PDqiB0YW1iw6ltIGNvbmNvcmRhIHF1ZSBvIFJlcG9zaXTDs3JpbyBJbnN0aXR1Y2lvbmFsIGRhIFVGTUcgcG9kZSBtYW50ZXIgbWFpcyBkZSB1bWEgY8OzcGlhIGRlIHN1YSBwdWJsaWNhw6fDo28gcGFyYSBmaW5zIGRlIHNlZ3VyYW7Dp2EsIGJhY2stdXAgZSBwcmVzZXJ2YcOnw6NvLgoKVm9jw6ogZGVjbGFyYSBxdWUgYSBzdWEgcHVibGljYcOnw6NvIMOpIG9yaWdpbmFsIGUgcXVlIHZvY8OqIHRlbSBvIHBvZGVyIGRlIGNvbmNlZGVyIG9zIGRpcmVpdG9zIGNvbnRpZG9zIG5lc3RhIGxpY2Vuw6dhLiBWb2PDqiB0YW1iw6ltIGRlY2xhcmEgcXVlIG8gZGVww7NzaXRvIGRlIHN1YSBwdWJsaWNhw6fDo28gbsOjbywgcXVlIHNlamEgZGUgc2V1IGNvbmhlY2ltZW50bywgaW5mcmluZ2UgZGlyZWl0b3MgYXV0b3JhaXMgZGUgbmluZ3XDqW0uCgpDYXNvIGEgc3VhIHB1YmxpY2HDp8OjbyBjb250ZW5oYSBtYXRlcmlhbCBxdWUgdm9jw6ogbsOjbyBwb3NzdWkgYSB0aXR1bGFyaWRhZGUgZG9zIGRpcmVpdG9zIGF1dG9yYWlzLCB2b2PDqiBkZWNsYXJhIHF1ZSBvYnRldmUgYSBwZXJtaXNzw6NvIGlycmVzdHJpdGEgZG8gZGV0ZW50b3IgZG9zIGRpcmVpdG9zIGF1dG9yYWlzIHBhcmEgY29uY2VkZXIgYW8gUmVwb3NpdMOzcmlvIEluc3RpdHVjaW9uYWwgZGEgVUZNRyBvcyBkaXJlaXRvcyBhcHJlc2VudGFkb3MgbmVzdGEgbGljZW7Dp2EsIGUgcXVlIGVzc2UgbWF0ZXJpYWwgZGUgcHJvcHJpZWRhZGUgZGUgdGVyY2Vpcm9zIGVzdMOhIGNsYXJhbWVudGUgaWRlbnRpZmljYWRvIGUgcmVjb25oZWNpZG8gbm8gdGV4dG8gb3Ugbm8gY29udGXDumRvIGRhIHB1YmxpY2HDp8OjbyBvcmEgZGVwb3NpdGFkYS4KCkNBU08gQSBQVUJMSUNBw4fDg08gT1JBIERFUE9TSVRBREEgVEVOSEEgU0lETyBSRVNVTFRBRE8gREUgVU0gUEFUUk9Dw41OSU8gT1UgQVBPSU8gREUgVU1BIEFHw4pOQ0lBIERFIEZPTUVOVE8gT1UgT1VUUk8gT1JHQU5JU01PLCBWT0PDiiBERUNMQVJBIFFVRSBSRVNQRUlUT1UgVE9ET1MgRSBRVUFJU1FVRVIgRElSRUlUT1MgREUgUkVWSVPDg08gQ09NTyBUQU1Cw4lNIEFTIERFTUFJUyBPQlJJR0HDh8OVRVMgRVhJR0lEQVMgUE9SIENPTlRSQVRPIE9VIEFDT1JETy4KCk8gUmVwb3NpdMOzcmlvIEluc3RpdHVjaW9uYWwgZGEgVUZNRyBzZSBjb21wcm9tZXRlIGEgaWRlbnRpZmljYXIgY2xhcmFtZW50ZSBvIHNldSBub21lKHMpIG91IG8ocykgbm9tZXMocykgZG8ocykgZGV0ZW50b3IoZXMpIGRvcyBkaXJlaXRvcyBhdXRvcmFpcyBkYSBwdWJsaWNhw6fDo28sIGUgbsOjbyBmYXLDoSBxdWFscXVlciBhbHRlcmHDp8OjbywgYWzDqW0gZGFxdWVsYXMgY29uY2VkaWRhcyBwb3IgZXN0YSBsaWNlbsOnYS4KCg==Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2020-11-17T13:19:08Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Dynamic volatility models for market risk and portfolio analysis |
title |
Dynamic volatility models for market risk and portfolio analysis |
spellingShingle |
Dynamic volatility models for market risk and portfolio analysis Arthur Tarso Rego Financial Time Series Dynamic Models Multivariate Stochastic Models Estatística – Teses. Analise de séries temporais – Teses. Processo estocástico – Teses. Modelo dinâmico – Teses. Análise multivariada - Teses. |
title_short |
Dynamic volatility models for market risk and portfolio analysis |
title_full |
Dynamic volatility models for market risk and portfolio analysis |
title_fullStr |
Dynamic volatility models for market risk and portfolio analysis |
title_full_unstemmed |
Dynamic volatility models for market risk and portfolio analysis |
title_sort |
Dynamic volatility models for market risk and portfolio analysis |
author |
Arthur Tarso Rego |
author_facet |
Arthur Tarso Rego |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Thiago Rezende Dos Santos |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/9458275921031976 |
dc.contributor.referee1.fl_str_mv |
Fábio Nogueira Demarqui |
dc.contributor.referee2.fl_str_mv |
Cristiano de Carvalho Santos |
dc.contributor.referee3.fl_str_mv |
Fernando Ferraz do Nascimento |
dc.contributor.referee4.fl_str_mv |
João Batista de Morais Pereira |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4493838388491868 |
dc.contributor.author.fl_str_mv |
Arthur Tarso Rego |
contributor_str_mv |
Thiago Rezende Dos Santos Fábio Nogueira Demarqui Cristiano de Carvalho Santos Fernando Ferraz do Nascimento João Batista de Morais Pereira |
dc.subject.por.fl_str_mv |
Financial Time Series Dynamic Models Multivariate Stochastic Models |
topic |
Financial Time Series Dynamic Models Multivariate Stochastic Models Estatística – Teses. Analise de séries temporais – Teses. Processo estocástico – Teses. Modelo dinâmico – Teses. Análise multivariada - Teses. |
dc.subject.other.pt_BR.fl_str_mv |
Estatística – Teses. Analise de séries temporais – Teses. Processo estocástico – Teses. Modelo dinâmico – Teses. Análise multivariada - Teses. |
description |
Dealing with financial time series brings many challenges to data modeling, given the existence of heavy tails and extreme return values caused by external events, such as politics, natural disasters, economical events, or even speculation. Providing reliable and interpretable insights to market agents, based on statistical models, to base strategic decisions on building assets portfolios, taking arbitrage decisions, and managing investment risks is crucial for avoiding losses and correctly pricing assets for developing successful investment strategies. Rego and Santos (2020) proposed the Non-Gaussian Stochastic Volatility Model with Jumps (NGSVJ) for market volatility evaluation, which includes automatic inference procedure that allows the model to be fast enough to bring tangible results for the user, using an ordinary home computer, to perform trading operations. The Dynamic Models (DM) class, on which the NGSVJ is based, has a flexible structure that enables the inclusion of new features on the models and has implementation simplicity from the computational perspective. The DM class of models is still unexplored for financial applications when compared to the other classes of models commonly used on literature, mainly based on Stochastic Volatility (SV) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) classes of models. In this thesis, several developments are made using as basis the DM class and the NGSVJ model. For dealing with a single asset, or univariate, financial time series, developments are made to the NGSVJ to be able to estimate the degree of freedom of the gamma mixture parameter and include a Hiden Markov (HMM) to give flexibility and interpretability to the model for applications on arbitrage intraday market operations. For dealing with multiple assets portfolio, or multivariate, financial time series the Multivariate Stochastic Volatility Model with Jumps (MSVJ) was developed, based on DM structure, to enable financial agents to estimate the volatility and correlation between portfolio assets and effectively develop a risk management strategy. This thesis provides a wide set of statistical models, based on DM class, that can be used in finance for taking arbitrage and investment decisions, whether it is used for analyzing a single asset or a portfolio. Simulation studies are presented as well as applications on the S\&P 500 market index, commodity derivatives, and exchange rates, to illustrate model performance. The proposed models have highly interpretable results, bringing major developments to the DM class of models and their applications on finance. The proposed models are robust in the sense to incorporate several stylized characteristics of return data, bringing major developments to the NGSVJ and their applications. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-11-17T13:19:08Z |
dc.date.available.fl_str_mv |
2020-11-17T13:19:08Z |
dc.date.issued.fl_str_mv |
2020-08-28 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/34384 |
url |
http://hdl.handle.net/1843/34384 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/3.0/pt/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/3.0/pt/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Estatística |
dc.publisher.initials.fl_str_mv |
UFMG |
dc.publisher.country.fl_str_mv |
Brasil |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
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UFMG |
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Repositório Institucional da UFMG |
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