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Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall

Although quantile regression to calculate risk measures is widely established in the financial literature, when considering data observed at mixed-frequency, an extension is needed. In this paper, a model is built on a mixed-frequency quantile regressions to directly estimate the Value-at-Risk (VaR)...

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Autores principales: Candila, Vincenzo, Gallo, Giampiero M., Petrella, Lea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191104/
https://www.ncbi.nlm.nih.gov/pubmed/37361082
http://dx.doi.org/10.1007/s10479-023-05370-x
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author Candila, Vincenzo
Gallo, Giampiero M.
Petrella, Lea
author_facet Candila, Vincenzo
Gallo, Giampiero M.
Petrella, Lea
author_sort Candila, Vincenzo
collection PubMed
description Although quantile regression to calculate risk measures is widely established in the financial literature, when considering data observed at mixed-frequency, an extension is needed. In this paper, a model is built on a mixed-frequency quantile regressions to directly estimate the Value-at-Risk (VaR) and the Expected Shortfall (ES) measures. In particular, the low-frequency component incorporates information coming from variables observed at, typically, monthly or lower frequencies, while the high-frequency component can include a variety of daily variables, like market indices or realized volatility measures. The conditions for the weak stationarity of the daily return process are derived and the finite sample properties are investigated in an extensive Monte Carlo exercise. The validity of the proposed model is then explored through a real data application using two energy commodities, namely, Crude Oil and Gasoline futures. Results show that our model outperforms other competing specifications, on the basis of some popular VaR and ES backtesting test procedures.
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spelling pubmed-101911042023-05-19 Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall Candila, Vincenzo Gallo, Giampiero M. Petrella, Lea Ann Oper Res Original Research Although quantile regression to calculate risk measures is widely established in the financial literature, when considering data observed at mixed-frequency, an extension is needed. In this paper, a model is built on a mixed-frequency quantile regressions to directly estimate the Value-at-Risk (VaR) and the Expected Shortfall (ES) measures. In particular, the low-frequency component incorporates information coming from variables observed at, typically, monthly or lower frequencies, while the high-frequency component can include a variety of daily variables, like market indices or realized volatility measures. The conditions for the weak stationarity of the daily return process are derived and the finite sample properties are investigated in an extensive Monte Carlo exercise. The validity of the proposed model is then explored through a real data application using two energy commodities, namely, Crude Oil and Gasoline futures. Results show that our model outperforms other competing specifications, on the basis of some popular VaR and ES backtesting test procedures. Springer US 2023-05-17 /pmc/articles/PMC10191104/ /pubmed/37361082 http://dx.doi.org/10.1007/s10479-023-05370-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Candila, Vincenzo
Gallo, Giampiero M.
Petrella, Lea
Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
title Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
title_full Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
title_fullStr Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
title_full_unstemmed Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
title_short Mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
title_sort mixed-frequency quantile regressions to forecast value-at-risk and expected shortfall
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191104/
https://www.ncbi.nlm.nih.gov/pubmed/37361082
http://dx.doi.org/10.1007/s10479-023-05370-x
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