Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model

Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model

This paper investigates the dependence structure between the Air Pollution Index (API) of Shenzhen and corresponding regional, national levels based on copula based GARCH models. In particular, time varying normal copula and time varying SJC copula are compared and employed to model the dependence s...

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Main Authors: Zhanqiong H., Sriboonchitta S., Jing D.
Format: Conference or Workshop Item
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-84872800207&partnerID=40&md5=07a43b5014826a05ed5901ed7bead696
http://cmuir.cmu.ac.th/handle/6653943832/1226
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-12262014-08-29T09:20:22Z Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model Zhanqiong H. Sriboonchitta S. Jing D. This paper investigates the dependence structure between the Air Pollution Index (API) of Shenzhen and corresponding regional, national levels based on copula based GARCH models. In particular, time varying normal copula and time varying SJC copula are compared and employed to model the dependence structure. Comparison with the results of DCC-GARCH model is made. We find that there exists significant asymmetric upper and lower tail dependence between Shenzhen and regional, national levels; tail dependence captures the change in dependence better; dependence structure change across time. Our findings have implications for environmental management. ? 2013 Springer-Verlag Berlin Heidelberg. 2014-08-29T09:20:22Z 2014-08-29T09:20:22Z 2013 Conference Paper 9.78364E+12 21945357 10.1007/978-3-642-35443-4-15 95102 http://www.scopus.com/inward/record.url?eid=2-s2.0-84872800207&partnerID=40&md5=07a43b5014826a05ed5901ed7bead696 http://cmuir.cmu.ac.th/handle/6653943832/1226 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description This paper investigates the dependence structure between the Air Pollution Index (API) of Shenzhen and corresponding regional, national levels based on copula based GARCH models. In particular, time varying normal copula and time varying SJC copula are compared and employed to model the dependence structure. Comparison with the results of DCC-GARCH model is made. We find that there exists significant asymmetric upper and lower tail dependence between Shenzhen and regional, national levels; tail dependence captures the change in dependence better; dependence structure change across time. Our findings have implications for environmental management. ? 2013 Springer-Verlag Berlin Heidelberg.
format Conference or Workshop Item
author Zhanqiong H.
Sriboonchitta S.
Jing D.
spellingShingle Zhanqiong H.
Sriboonchitta S.
Jing D.
Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model
author_facet Zhanqiong H.
Sriboonchitta S.
Jing D.
author_sort Zhanqiong H.
title Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model
title_short Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model
title_full Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model
title_fullStr Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model
title_full_unstemmed Modeling dependence dynamics of air pollution: Time series analysis using a copula based GARCH type model
title_sort modeling dependence dynamics of air pollution: time series analysis using a copula based garch type model
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-84872800207&partnerID=40&md5=07a43b5014826a05ed5901ed7bead696
http://cmuir.cmu.ac.th/handle/6653943832/1226
_version_ 1681419630912995328

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