Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.
Korea has increased its self-sufficiency in natural resources since 2006. Accordingly, the number of overseas resource developments has also increased; however, the quantification of the economic impact of such developments is rarely studied. Therefore, we analyse the economic impact on the Korean economy and create a hypothetical Australian coal bed methane development project. As there is no overseas resource development sector in the Korean input–output table, we identify the relationships between the project and Korean industries using cost data and develop a project–industry relationship diagram. Next, we analyse the economic impacts of overseas coal bed methane development on the Korean economy, especially the production inducement, value added, and labour inducement effects. According to our results, the production inducement effect on the Korean economy is estimated to be 643,360 million Korean Won annually with 1.918 of production inducement coefficient when Korean companies take full charge of the project.
Although natural gas contracts have been done based on oil prices, many recent research argued that the natural gas and oil prices are decoupled. Therefore, it is necessary to analyze the natural gas price as an independent price. In the light of this, we forecasted the short term natural gas price using wavelet decomposition method. In order to compare a forecasting power, three approaches are applied. The first approach is ARIMA only case. We applied an ARIMA into the original data. The second one is wavelet-ARIMA case. In this case, ARIMA is applied into the approximation. In the third approach, we implement wavelet-GARCH analysis. In this approach, an approximation is treated same as second case, but GARCH is applied to details. As results, the wavelet-applied approaches are better than only ARIMA case for the forecasting power. Consequently, we could confirm the usefulness of wavelet-applied approaches in natural gas price forecasting.
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