Daily Growth at Risk: financial or real drivers? The answer is not always the same

We propose a daily growth-at-risk (GaR) approach based on high-frequency financial and real indicators for monitoring downside risks in the US economy. We show that the relative importance of these indicators in terms of their forecasting power is time varying. Indeed, the optimal forecasting weight...

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Autores: Chuliá Soler, Helena, Garrón Vedia, Ignacio, Uribe Gil, Jorge Mario
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/213280
Acceso en línea:https://hdl.handle.net/2445/213280
Access Level:acceso abierto
Palabra clave:Risc (Economia)
Variables aleatòries
Aprenentatge automàtic
Valor (Economia)
Risk
Random variables
Machine learning
Value (Economics)
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oai_identifier_str oai:recercat.cat:2445/213280
network_acronym_str ES
network_name_str España
spelling Daily Growth at Risk: financial or real drivers? The answer is not always the same Chuliá Soler, Helena Garrón Vedia, Ignacio Uribe Gil, Jorge Mario Risc (Economia) Variables aleatòries Aprenentatge automàtic Valor (Economia) Risk Random variables Machine learning Value (Economics) We propose a daily growth-at-risk (GaR) approach based on high-frequency financial and real indicators for monitoring downside risks in the US economy. We show that the relative importance of these indicators in terms of their forecasting power is time varying. Indeed, the optimal forecasting weights of our variables differed clearly between the Global Financial Crisis and the recent Covid-19 crisis, reflecting the dissimilar nature of these two events. We introduce LASSO, elastic net, and adaptive sparse group LASSO into the family of mixed data sampling models used to estimate GaR and show how they outperform previous candidates explored in the literature. Moreover, equity market volatility, credit spreads, and the Aruoba–Diebold–Scotti business conditions index are found to be relevant indicators for nowcasting economic activity, especially during episodes of crisis. Overall, our results show that daily information about both real and financial variables is key for producing accurate point and tail-risk nowcasts of economic activity. Elsevier B.V. https://hdl.handle.net/2445/213280
title Daily Growth at Risk: financial or real drivers? The answer is not always the same
spellingShingle Daily Growth at Risk: financial or real drivers? The answer is not always the same
Chuliá Soler, Helena
Risc (Economia)
Variables aleatòries
Aprenentatge automàtic
Valor (Economia)
Risk
Random variables
Machine learning
Value (Economics)
title_short Daily Growth at Risk: financial or real drivers? The answer is not always the same
title_full Daily Growth at Risk: financial or real drivers? The answer is not always the same
title_fullStr Daily Growth at Risk: financial or real drivers? The answer is not always the same
title_full_unstemmed Daily Growth at Risk: financial or real drivers? The answer is not always the same
title_sort Daily Growth at Risk: financial or real drivers? The answer is not always the same
author Chuliá Soler, Helena
author_facet Chuliá Soler, Helena
Garrón Vedia, Ignacio
Uribe Gil, Jorge Mario
author_role author
author2 Garrón Vedia, Ignacio
Uribe Gil, Jorge Mario
author2_role author
author
topic Risc (Economia)
Variables aleatòries
Aprenentatge automàtic
Valor (Economia)
Risk
Random variables
Machine learning
Value (Economics)
topic_facet Risc (Economia)
Variables aleatòries
Aprenentatge automàtic
Valor (Economia)
Risk
Random variables
Machine learning
Value (Economics)
description We propose a daily growth-at-risk (GaR) approach based on high-frequency financial and real indicators for monitoring downside risks in the US economy. We show that the relative importance of these indicators in terms of their forecasting power is time varying. Indeed, the optimal forecasting weights of our variables differed clearly between the Global Financial Crisis and the recent Covid-19 crisis, reflecting the dissimilar nature of these two events. We introduce LASSO, elastic net, and adaptive sparse group LASSO into the family of mixed data sampling models used to estimate GaR and show how they outperform previous candidates explored in the literature. Moreover, equity market volatility, credit spreads, and the Aruoba–Diebold–Scotti business conditions index are found to be relevant indicators for nowcasting economic activity, especially during episodes of crisis. Overall, our results show that daily information about both real and financial variables is key for producing accurate point and tail-risk nowcasts of economic activity.
publishDate 2024
format article
status_str acceptedVersion
url https://hdl.handle.net/2445/213280
eu_rights_str_mv openAccess
publisher Elsevier B.V.
institution Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
collection Recercat. Dipósit de la Recerca de Catalunya
reponame_str Recercat. Dipósit de la Recerca de Catalunya
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
_version_ 1878734662158778368
publishDateSort 2024
author_browse Chuliá Soler, Helena
Garrón Vedia, Ignacio
Uribe Gil, Jorge Mario
publisherStr Elsevier B.V.
score 6,9008884

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