An Application of the IFM Method for the Risk Assessment of Financial Instruments

External influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the futu...

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Autores: Pons, Adrià, Cristóbal Fransi, Eduard, Vintró Sánchez, Carla, Rius Torrentó, Josep Maria, Querol, Oriol, Vilaplana Mayoral, Jordi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/72331
Acceso en línea:https://doi.org/10.1007/s10614-021-10208-4
http://hdl.handle.net/10459.1/72331
Access Level:acceso abierto
Palabra clave:Risk simulation
Monte carlo
GARCH
t-Copula
VaR
Risk tolerance
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spelling An Application of the IFM Method for the Risk Assessment of Financial InstrumentsPons, AdriàCristóbal Fransi, EduardVintró Sánchez, CarlaRius Torrentó, Josep MariaQuerol, OriolVilaplana Mayoral, JordiRisk simulationMonte carloGARCHt-CopulaVaRRisk toleranceExternal influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the future to assess the risks and determine the potential loss of a security in the stock market, thus reducing reasoning influenced by feelings for bank and financial firms seeking to deploy AI and advanced automation. We used the IFM (inference function for margins) method in different market scenarios, with particular emphasis on the strengths and weaknesses of it. The study is assessed on single product level with the skewed studen-t GARCH(1,1) model and portfolio level with t-copulas for the inter-dependencies. It has been shown that under normal market conditions the risk is predicted properly for both levels. However, when an unexpected market event occurs, the prediction fails. To address this limitation, a combined model with sentiment analysis and regression is proposed for further investigation as a future work.The authors are pleased to acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (Grant id.: TURCOLAB ECO2017-88984-R). Also, this work was supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2017-84553- C2-2-R. Finally, some of the authors are members of the research group 2017-SGR363, funded by the Generalitat de Catalunya.Springer2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1007/s10614-021-10208-4http://hdl.handle.net/10459.1/72331reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84553-C2-2-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ECO2017-88984-RReproducció del document publicat a https://doi.org/10.1007/s10614-021-10208-4Computational Economics, 2021cc-by (c) Pons et al., 2021info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/723312026-06-24T12:42:17Z
dc.title.none.fl_str_mv An Application of the IFM Method for the Risk Assessment of Financial Instruments
title An Application of the IFM Method for the Risk Assessment of Financial Instruments
spellingShingle An Application of the IFM Method for the Risk Assessment of Financial Instruments
Pons, Adrià
Risk simulation
Monte carlo
GARCH
t-Copula
VaR
Risk tolerance
title_short An Application of the IFM Method for the Risk Assessment of Financial Instruments
title_full An Application of the IFM Method for the Risk Assessment of Financial Instruments
title_fullStr An Application of the IFM Method for the Risk Assessment of Financial Instruments
title_full_unstemmed An Application of the IFM Method for the Risk Assessment of Financial Instruments
title_sort An Application of the IFM Method for the Risk Assessment of Financial Instruments
dc.creator.none.fl_str_mv Pons, Adrià
Cristóbal Fransi, Eduard
Vintró Sánchez, Carla
Rius Torrentó, Josep Maria
Querol, Oriol
Vilaplana Mayoral, Jordi
author Pons, Adrià
author_facet Pons, Adrià
Cristóbal Fransi, Eduard
Vintró Sánchez, Carla
Rius Torrentó, Josep Maria
Querol, Oriol
Vilaplana Mayoral, Jordi
author_role author
author2 Cristóbal Fransi, Eduard
Vintró Sánchez, Carla
Rius Torrentó, Josep Maria
Querol, Oriol
Vilaplana Mayoral, Jordi
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Risk simulation
Monte carlo
GARCH
t-Copula
VaR
Risk tolerance
topic Risk simulation
Monte carlo
GARCH
t-Copula
VaR
Risk tolerance
description External influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the future to assess the risks and determine the potential loss of a security in the stock market, thus reducing reasoning influenced by feelings for bank and financial firms seeking to deploy AI and advanced automation. We used the IFM (inference function for margins) method in different market scenarios, with particular emphasis on the strengths and weaknesses of it. The study is assessed on single product level with the skewed studen-t GARCH(1,1) model and portfolio level with t-copulas for the inter-dependencies. It has been shown that under normal market conditions the risk is predicted properly for both levels. However, when an unexpected market event occurs, the prediction fails. To address this limitation, a combined model with sentiment analysis and regression is proposed for further investigation as a future work.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1007/s10614-021-10208-4
http://hdl.handle.net/10459.1/72331
url https://doi.org/10.1007/s10614-021-10208-4
http://hdl.handle.net/10459.1/72331
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84553-C2-2-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ECO2017-88984-R
Reproducció del document publicat a https://doi.org/10.1007/s10614-021-10208-4
Computational Economics, 2021
dc.rights.none.fl_str_mv cc-by (c) Pons et al., 2021
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Pons et al., 2021
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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