Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession

Detalhes bibliográficos
Autor(a) principal: Neves, Gonçalo de Sousa
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/145556
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
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spelling Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recessionCOVID-19Financial LossPandemic RecessionStock IndexesValue at RiskDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementThe purpose of this study is two-fold. First, it aims at providing a theoretical overview of the most widely adopted methods for forecasting Value-at-Risk (VaR). Second, through a practical implementation, it proposes a methodology to compare and evaluate the predictive ability of different parametric, non-parametric and semi-parametric models to capture the market losses incurred during the COVID-19 pandemic recession of 2020. To evaluate these models, it is applied a two-staged backtesting procedure based on accuracy statistical tests and loss functions. VaR forecasts are evaluated during a volatile and a stable forecasting periods. The results of the study suggest that, for the volatile period, the Extreme Value Theory with a peaks over threshold (EVT-POT) approach produces the most accurate VaR forecasts across all different methodologies. The Filtered Historical Simulation (FHS), Volatility Weighted Historical Simulation (VWHS) and the Glosten, Jagannathan and Runkle (GJR) GARCH with skewed generalized error distribution (GJR GARCH–SGED) models also produce satisfactory forecasts. Moreover, other parametric approaches, namely the GARCH and EWMA, despite less accurate, also produce reliable results. Furthermore, the overall performance of all models improves significantly during the stable forecasting period. For instance, the Historical Simulation with exponentially decreasing weights (BRW HS), one of the worst performers during the volatile forecasting period, produces the most accurate VaR forecasts, with the lowest penalty scores, during the stable forecasting period. Lastly, it was also found that as the level of conservativeness of the model increases, the overestimation of the actual incurred risk seems to a be recurrent event.Baptista, Maria Helena Miranda FloresRUNNeves, Gonçalo de Sousa2022-11-16T11:14:30Z2022-10-252022-10-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145556TID:203097955enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:26:03Zoai:run.unl.pt:10362/145556Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:08.905456Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
title Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
spellingShingle Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
Neves, Gonçalo de Sousa
COVID-19
Financial Loss
Pandemic Recession
Stock Indexes
Value at Risk
title_short Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
title_full Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
title_fullStr Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
title_full_unstemmed Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
title_sort Models for Forecasting Value at Risk: A comparison of the predictive ability of different VaR models to capture market losses incurred during the 2020 pandemic recession
author Neves, Gonçalo de Sousa
author_facet Neves, Gonçalo de Sousa
author_role author
dc.contributor.none.fl_str_mv Baptista, Maria Helena Miranda Flores
RUN
dc.contributor.author.fl_str_mv Neves, Gonçalo de Sousa
dc.subject.por.fl_str_mv COVID-19
Financial Loss
Pandemic Recession
Stock Indexes
Value at Risk
topic COVID-19
Financial Loss
Pandemic Recession
Stock Indexes
Value at Risk
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
publishDate 2022
dc.date.none.fl_str_mv 2022-11-16T11:14:30Z
2022-10-25
2022-10-25T00:00:00Z
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://hdl.handle.net/10362/145556
TID:203097955
url http://hdl.handle.net/10362/145556
identifier_str_mv TID:203097955
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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