Modeling the relationship between climate oscillations and drought by a multivariate GARCH model.
Typical multivariate time series models may exhibit comovement in mean but not in variance of hydrologic and climatic variables. This paper introduces multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models to capture the comovement of the variance or the conditional co...
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Online Access: | https://espace.inrs.ca/id/eprint/3633/ https://espace.inrs.ca/id/eprint/3633/1/P2477.pdf https://doi.org/10.1002/2013WR013810 |
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ftinrsquebec:oai:espace.inrs.ca:3633 2023-05-15T17:35:27+02:00 Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. Modarres, Reza Ouarda, Taha B. M. J. 2014 application/pdf https://espace.inrs.ca/id/eprint/3633/ https://espace.inrs.ca/id/eprint/3633/1/P2477.pdf https://doi.org/10.1002/2013WR013810 en eng https://espace.inrs.ca/id/eprint/3633/1/P2477.pdf Modarres, Reza et Ouarda, Taha B. M. J. (2014). Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. Water Resources Research , vol. 50 , nº 1. p. 601-618. DOI:10.1002/2013WR013810 <https://doi.org/10.1002/2013WR013810>. doi:10.1002/2013WR013810 bivariate GARCH conditional covariance diagonal BEKK diagonal VECH drought NAO nonlinearity SOI Article Évalué par les pairs 2014 ftinrsquebec https://doi.org/10.1002/2013WR013810 2023-02-10T11:42:35Z Typical multivariate time series models may exhibit comovement in mean but not in variance of hydrologic and climatic variables. This paper introduces multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models to capture the comovement of the variance or the conditional covariance between two hydroclimatic time series. The diagonal vectorized and Baba-Engle-Kroft-Kroner models are developed to evaluate the covariance between drought and two atmospheric circulations, Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO) time series during 1954-2000. The univariate generalized autoregressive conditional heteroscedasticity model indicates a strong persistency level in conditional variance for NAO and a moderate persistency level for SOI. The conditional variance of short-term drought index indicates low level of persistency, while the long-term index drought indicates high level of persistency in conditional variance. The estimated conditional covariance between drought and atmospheric indices is shown to be weak and negative. It is also observed that the covariance between drought and atmospheric indices is largely dependent on short-run variance of atmospheric indices rather than their long-run variance. The nonlinearity and stationarity tests show that the conditional covariances are nonlinear but stationary. However, the degree of nonlinearity is higher for the covariance between long-term drought and atmospheric indices. It is also observed that the nonlinearity of NAO is higher than that for SOI, in contrast to the stationarity which is stronger for SOI time series. Key Points Multivariate heteroscedastic models are developed for drought analysis Conditional covariance between drought, SOI, and NAO is not strong Time-varying correlations between drought and atmospheric indices are estimated. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Institut national de la recherche scientifique, Québec: Espace INRS Soi ENVELOPE(30.704,30.704,66.481,66.481) Water Resources Research 50 1 601 618 |
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Open Polar |
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Institut national de la recherche scientifique, Québec: Espace INRS |
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ftinrsquebec |
language |
English |
topic |
bivariate GARCH conditional covariance diagonal BEKK diagonal VECH drought NAO nonlinearity SOI |
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bivariate GARCH conditional covariance diagonal BEKK diagonal VECH drought NAO nonlinearity SOI Modarres, Reza Ouarda, Taha B. M. J. Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. |
topic_facet |
bivariate GARCH conditional covariance diagonal BEKK diagonal VECH drought NAO nonlinearity SOI |
description |
Typical multivariate time series models may exhibit comovement in mean but not in variance of hydrologic and climatic variables. This paper introduces multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models to capture the comovement of the variance or the conditional covariance between two hydroclimatic time series. The diagonal vectorized and Baba-Engle-Kroft-Kroner models are developed to evaluate the covariance between drought and two atmospheric circulations, Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO) time series during 1954-2000. The univariate generalized autoregressive conditional heteroscedasticity model indicates a strong persistency level in conditional variance for NAO and a moderate persistency level for SOI. The conditional variance of short-term drought index indicates low level of persistency, while the long-term index drought indicates high level of persistency in conditional variance. The estimated conditional covariance between drought and atmospheric indices is shown to be weak and negative. It is also observed that the covariance between drought and atmospheric indices is largely dependent on short-run variance of atmospheric indices rather than their long-run variance. The nonlinearity and stationarity tests show that the conditional covariances are nonlinear but stationary. However, the degree of nonlinearity is higher for the covariance between long-term drought and atmospheric indices. It is also observed that the nonlinearity of NAO is higher than that for SOI, in contrast to the stationarity which is stronger for SOI time series. Key Points Multivariate heteroscedastic models are developed for drought analysis Conditional covariance between drought, SOI, and NAO is not strong Time-varying correlations between drought and atmospheric indices are estimated. |
format |
Article in Journal/Newspaper |
author |
Modarres, Reza Ouarda, Taha B. M. J. |
author_facet |
Modarres, Reza Ouarda, Taha B. M. J. |
author_sort |
Modarres, Reza |
title |
Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. |
title_short |
Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. |
title_full |
Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. |
title_fullStr |
Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. |
title_full_unstemmed |
Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. |
title_sort |
modeling the relationship between climate oscillations and drought by a multivariate garch model. |
publishDate |
2014 |
url |
https://espace.inrs.ca/id/eprint/3633/ https://espace.inrs.ca/id/eprint/3633/1/P2477.pdf https://doi.org/10.1002/2013WR013810 |
long_lat |
ENVELOPE(30.704,30.704,66.481,66.481) |
geographic |
Soi |
geographic_facet |
Soi |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://espace.inrs.ca/id/eprint/3633/1/P2477.pdf Modarres, Reza et Ouarda, Taha B. M. J. (2014). Modeling the relationship between climate oscillations and drought by a multivariate GARCH model. Water Resources Research , vol. 50 , nº 1. p. 601-618. DOI:10.1002/2013WR013810 <https://doi.org/10.1002/2013WR013810>. doi:10.1002/2013WR013810 |
op_doi |
https://doi.org/10.1002/2013WR013810 |
container_title |
Water Resources Research |
container_volume |
50 |
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1 |
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601 |
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618 |
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1766134616765235200 |