Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets

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Purpose. This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods. Design/methodology/approach. Many VaR estimation models have bee...

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Detalles Bibliográficos
Autores: Serrano Bautista, Ramona, Núñez Mora, José Antonio
Formato: artículo
Fecha de Publicación:2021
Institución:Universidad ESAN
Repositorio:ESAN-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.esan.edu.pe:20.500.12640/2829
Enlace del recurso:https://revistas.esan.edu.pe/index.php/jefas/article/view/557
https://hdl.handle.net/20.500.12640/2829
https://doi.org/10.1108/JEFAS-03-2021-0009
Nivel de acceso:acceso abierto
Materia:Value at risk
GARCH
CaViaR
MILA
ASEAN
Valor en riesgo
https://purl.org/pe-repo/ocde/ford#5.02.04
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dc.title.en_EN.fl_str_mv Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
title Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
spellingShingle Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
Serrano Bautista, Ramona
Value at risk
GARCH
CaViaR
MILA
ASEAN
Valor en riesgo
GARCH
CaViaR
MILA
ASEAN
https://purl.org/pe-repo/ocde/ford#5.02.04
title_short Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
title_full Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
title_fullStr Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
title_full_unstemmed Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
title_sort Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
author Serrano Bautista, Ramona
author_facet Serrano Bautista, Ramona
Núñez Mora, José Antonio
author_role author
author2 Núñez Mora, José Antonio
author2_role author
dc.contributor.author.fl_str_mv Serrano Bautista, Ramona
Núñez Mora, José Antonio
dc.subject.en_EN.fl_str_mv Value at risk
GARCH
CaViaR
MILA
ASEAN
topic Value at risk
GARCH
CaViaR
MILA
ASEAN
Valor en riesgo
GARCH
CaViaR
MILA
ASEAN
https://purl.org/pe-repo/ocde/ford#5.02.04
dc.subject.es_ES.fl_str_mv Valor en riesgo
GARCH
CaViaR
MILA
ASEAN
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.02.04
description Purpose. This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods. Design/methodology/approach. Many VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004). Findings. The results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy. Originality/value. An important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2022-01-25T20:02:03Z
dc.date.available.none.fl_str_mv 2022-01-25T20:02:03Z
dc.date.issued.fl_str_mv 2021-12-19
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dc.identifier.citation.none.fl_str_mv Serrano Bautista, R., & Núñez Mora, J. A. (2021). Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets. Journal of Economics, Finance and Administrative Science, 26(52), 197–221. https://doi.org/10.1108/JEFAS-03-2021-0009
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url https://revistas.esan.edu.pe/index.php/jefas/article/view/557
https://hdl.handle.net/20.500.12640/2829
https://doi.org/10.1108/JEFAS-03-2021-0009
identifier_str_mv Serrano Bautista, R., & Núñez Mora, J. A. (2021). Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets. Journal of Economics, Finance and Administrative Science, 26(52), 197–221. https://doi.org/10.1108/JEFAS-03-2021-0009
dc.language.none.fl_str_mv Inglés
dc.language.iso.none.fl_str_mv eng
language_invalid_str_mv Inglés
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spelling Serrano Bautista, RamonaNúñez Mora, José Antonio2022-01-25T20:02:03Z2022-01-25T20:02:03Z2021-12-19https://revistas.esan.edu.pe/index.php/jefas/article/view/557Serrano Bautista, R., & Núñez Mora, J. A. (2021). Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets. Journal of Economics, Finance and Administrative Science, 26(52), 197–221. https://doi.org/10.1108/JEFAS-03-2021-0009https://hdl.handle.net/20.500.12640/2829https://doi.org/10.1108/JEFAS-03-2021-0009Purpose. This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods. Design/methodology/approach. Many VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004). Findings. The results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy. Originality/value. An important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.Propósito: Este artículo prueba la exactitud de los modelos que predicen el valor en riesgo (VaR) para los mercados bursátiles emergentes de Mercado Integrado de América Latina (MILA) y la Asociación de Naciones del Sudeste Asiático (ASEAN) durante períodos de crisis. Diseño/metodología/enfoque: En la literatura se han presentado muchos modelos de estimación del VaR. En este artículo, el VaR se estima utilizando los modelos de heterocedasticidad condicional autorregresiva generalizada, EGARCH y GJR-GARCH bajo supuestos distribucionales normal, normal sesgado, t de Student y t de Student sesgado y se compara con el rendimiento predictivo del Valor en Riesgo Condicional Autorregresivo por Regresiones Cuantiles (CaViaR) considerando las cuatro especificaciones alternativas propuestas por Engle y Manganelli (2004). Hallazgos: Los resultados respaldan la solidez del modelo CaViaR en el pronóstico de VaR fuera de la muestra para los mercados bursátiles emergentes MILA y ASEAN-5 en períodos de crisis. Esta evidencia se basa en los resultados del enfoque de backtesting que analizó el rendimiento predictivo de los modelos según su precisión. Originalidad/valor: Una cuestión importante en el riesgo de mercado es la estimación inexacta del riesgo, ya que diferentes modelos de VaR conducen a diferentes medidas de riesgo, lo que significa que aún no existe un método aceptado para todas las situaciones y mercados. En particular, cuantificar y pronosticar el riesgo para los mercados bursátiles MILA y ASEAN-5 es crucial para evaluar el riesgo del mercado global, ya que MILA es la bolsa de valores más grande de América Latina y la región de la ASEAN representó el 11% del total de la inversión extranjera directa mundial. entradas en 2014. Además, según el Banco Asiático de Desarrollo, se prevé que esta región tenga un crecimiento anual promedio del 7% para 2025.application/pdfInglésengUniversidad ESAN. 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