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2014•Thai Journal of MathematicsOpen access

Investigation of the Dependence Structure Between Imports and Manufacturing Production Index of Thailand using Copula-Based GARCH Model

Chakorn Praprom, Songsak Sriboonchitta

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Abstract

This paper aims at investigating the dependence structure betweenthe imports and the manufacturing production index of Thailand using a copula-GARCH approach. We applied skewed student-t distribution to estimate all of themarginal distributions with ARMA(1,12)-GARCH(1,1), ARMA(1,2)-GARCH(1,1)to t the manufacturing production index (MPI) and the imports of Thailand.The results of this paper suggest that the student-t copula is the most appropriatemethod to best t the tail dependences in both static and time-varying copulasbecause the AIC and the BIC of this method are the lowest when compared withthe candidates among the other types of copula.

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What this paper is about

This paper aims at investigating the dependence structure betweenthe imports and the manufacturing production index of Thailand using a copula-GARCH approach. We applied skewed student-t distribution to estimate all of themarginal distributions with ARMA(1,12)-GARCH(1,1), ARMA(1,2)-GARCH(1,1)to t the manufacturing production index (MPI) and the imports of Thailand.The results of this paper suggest that the student-t copula is the most appropriatemethod to best t the tail dependences in both static and time-varying copulasbecause the AIC and the BIC of this method are the lowest when compared withthe candidates among the other types of copula.

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Available abstract

This paper aims at investigating the dependence structure betweenthe imports and the manufacturing production index of Thailand using a copula-GARCH approach. We applied skewed student-t distribution to estimate all of themarginal distributions with ARMA(1,12)-GARCH(1,1), ARMA(1,2)-GARCH(1,1)to t the manufacturing production index (MPI) and the imports of Thailand.The results of this paper suggest that the student-t copula is the most appropriatemethod to best t the tail dependences in both static and time-varying copulasbecause the AIC and the BIC of this method are the lowest when compared withthe candidates among the other types of copula.

Key concepts: Copula (linguistics), Autoregressive conditional heteroskedasticity, Econometrics, Mathematics, Index (typography), Statistics, Computer science, Volatility (finance)

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