Future Natural Gas Price Forecasting Model and Its Policy Implication

Ambya Ambya, Toto Gunarto, Ernie Hendrawaty, Fajrin Satria Dwi Kesumah and Febryan Kusuma Wisnu
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Ambya Ambya: Department of Development Economics, Faculty of Economics and Business, Universitas Lampung, Indonesia
Toto Gunarto: Department of Development Economics, Faculty of Economics and Business, Universitas Lampung, Indonesia
Ernie Hendrawaty: Department of Management, Faculty of Economics and Business, Universitas Lampung, Indonesia,
Fajrin Satria Dwi Kesumah: Department of Management, Faculty of Economics and Business, Universitas Lampung, Indonesia,
Febryan Kusuma Wisnu: Department of Agriculture Engineering, Faculty of Agriculture, Universitas Lampung, Indonesia

International Journal of Energy Economics and Policy, 2020, vol. 10, issue 5, 64-70

Abstract: Future natural gas (FNG) price is a collected data over the years and is a volatile movement in the market. In other words, FNG price is categorised as a time series data with volatility in both variance and mean, as well as most likely in some cases having heteroscedasticity problem. To come up with an estimated prediction model, some analysis tools, such as autoregressive integrated moving average (ARIMA) and generalised autoregressive conditional heteroscedasticity (GARCH), are introduced to find the best-fitted model having the smallest error value with high significance of probability value. This study aims to examine the best-fitted model that allows us to forecast FNG prices more accurately in the near future. There are 2842 observed data of daily FNG prices from 2009 to 2019 as the input of study objects. The finding suggests that the first measurement model of ARIMA (1,1,1) does not fit the model as having a non-significant probability value. Thus, it is required to check its heteroscedasticity by conducting an ARCH effect test. It is concluded that a data set has an effect of ARCH, so AR (p) GARCH (p,q) model is then tested. AR (1) GARCH (1,1) model is believed to be a best-fitted model having a significant P

Keywords: Future Natural Gas Price; Autoregressive Integrated Moving Average; Autoregressive Conditional Heteroscedastic Effect; Generalised Autoregressive Conditional Heteroscedasticity; Subsidy (search for similar items in EconPapers)
JEL-codes: C5 C53 H2 H25 Q4 Q47 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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