Presenter/Author Information

Felix Chan
Michael McAleer

Keywords

atmospheric carbon dioxide concentration, conditional volatility, forecasting, garch, gjr, egarch

Start Date

1-7-2004 12:00 AM

Description

Atmospheric carbon dioxide concentration (ACDC) is a crucial variable for many environmental simulationmodels, and is regarded as an important factor for predicting temperature and climate changes. However, theconditional variance of ACDC levels has not previously been examined. This paper analyses the trends and volatility inACDC levels using monthly data from January 1965 to December 2002. The data are a subset of the well known MaunaLoa atmosphere carbon dioxide record obtained through the Carbon Dioxide Information Analysis Center. Theconditional variance of ACDC levels is modelled using the generalised autoregressive conditional heteroscedasticity(GARCH) model and its asymmetric variations, namely the GJR and EGARCH models. These models are shown to beable to capture the dynamics in the conditional variance in ACDC levels and to improve the out-of-sample forecastaccuracy of ACDC.

Share

COinS
 
Jul 1st, 12:00 AM

Analysing Trends and Volatility in Atmospheric Carbon Dioxide Concentration Levels

Atmospheric carbon dioxide concentration (ACDC) is a crucial variable for many environmental simulationmodels, and is regarded as an important factor for predicting temperature and climate changes. However, theconditional variance of ACDC levels has not previously been examined. This paper analyses the trends and volatility inACDC levels using monthly data from January 1965 to December 2002. The data are a subset of the well known MaunaLoa atmosphere carbon dioxide record obtained through the Carbon Dioxide Information Analysis Center. Theconditional variance of ACDC levels is modelled using the generalised autoregressive conditional heteroscedasticity(GARCH) model and its asymmetric variations, namely the GJR and EGARCH models. These models are shown to beable to capture the dynamics in the conditional variance in ACDC levels and to improve the out-of-sample forecastaccuracy of ACDC.