Application of Short-Term Forecasting Models for Energy Entity Stock Price (Study on Indika Energi Tbk, Jii)

Azhar, Rialdi; Kesumah, Fajrin Satria Dwi; Ambya, Ambya; Wisnu, Febryan Kusuma; Russel, Edwin

doi:10.32479/ijeep.8715

Cited by 13 publications

(14 citation statements)

References 14 publications

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“…If the mean square error (MSE) and root mean square error (RMSE) are relatively small in association with the statistical description model, the models are assumed to have a well-fitted measurement to forecast (Azhar et al, 2020).…”

Section: The Mean and Variance Model Of Ar(p)-garch(pq)mentioning

confidence: 99%

Modelling and Forecasting Crude Oil Prices During Covid-19 Pandemic

Hendrawaty

Azhar

Kesumah

et al. 2021

IJEEP

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Currently, the world suffers from the COVID-19 pandemic, which affects almost every aspect of daily life, giving rise to recession and affecting the world prices of crude oil. The study aims to model the high uncertainty of volatility as well as to forecast the daily prices of crude oil during the pandemic. One econometric model applied in this study is the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) that allows more accurate and appropriate statistical analyses. Particularly, this study also discusses solving economic issues on the condition of any disturbances in the stability of daily crude oil prices. The findings suggest that the AR(1)-GARCH(1,1) model is a well-fitted model to predict relatively small errors. This model can act as a foundation for determining strategies in the future while facing such uncertain circumstances.

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Section: The Mean and Variance Model Of Ar(p)-garch(pq)mentioning

confidence: 99%

Modelling and Forecasting Crude Oil Prices During Covid-19 Pandemic

Hendrawaty

Azhar

Kesumah

et al. 2021

IJEEP

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“…Figure 4 for daily data on the natural gas industry with the Autocorrelation Function (ACF) shows that this series is non-stationary because ACF decays very slowly. Virginia, et al,and Azhar, et al, on research show that ACF can be used as an indicator that marks data to identify stationarity [7], [8].…”

Section: Check Stationarity Of Datamentioning

confidence: 99%

Implementation of Short-Term Forecasting Models in the Natural Gas Industry Financial Data

Azhar¹,

Sembiring²,

Muslimin³

et al. 2021

Proceedings of the First International Conference of Economics, Business &Amp; Entrepreneurship, ICEBE 2020, 1st October 2020,

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“…There are some ways to check the stationarity of data set both statistically and non-statically. Statistic measurement of stationary data can be tested by considering the results of the augmented Dickey-Fuller (ADF) test, autocorrelation function (ACF), partial autocorrelation (PACF) and distribution of normality data (Azhar et al, 2020). A visual graphical data set can also be one way to see whether a data set is stationary or not.…”

Section: Stationary Transformationmentioning

confidence: 99%

“…It is therefore necessary to run another test, that is, autoregressive conditional heteroscedasticity (ARCH) effect test (Azhar et al, 2020). Virginia et al (2018) argued that the effect can be tested using the Lagrange multiplier (LM) test, as heteroscedasticity is an issue in modelling time series data (Engle, 1982).…”

Section: Arch Effect Testsmentioning

confidence: 99%

“…However, this model has not been widely used in seeing their better performance in future values prediction compared to other related methods. Other studies focusing on more real-time forecasting models with short time in financial data such as daily price, interest rates and business cycle have been conducted by Skiadas et al (2017) and Azhar et al (2020).…”

Section: Introductionmentioning

confidence: 99%

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Future Natural Gas Price Forecasting Model and Its Policy Implication

Ambya

Gunarto

Hendrawaty

et al. 2020

IJEEP

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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 < 0.0001 with significantly small mean squared error and root mean squared error values, indicating that it has a very accurate prediction model. The forecasting model is to adjust the offered recommendation of policy for the government regarding the issue of high volatility of daily FNG prices in the future. We then offer a best-suited policy for some certain governments like Indonesia to give subsidy for targeted users in order to keep increasing their use of FNG that will expectedly affect their marketable product innovation and expansion, so economic growth in Indonesia is maintained.

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Application of Short-Term Forecasting Models for Energy Entity Stock Price (Study on Indika Energi Tbk, Jii)

Cited by 13 publications

References 14 publications

Modelling and Forecasting Crude Oil Prices During Covid-19 Pandemic

Modelling and Forecasting Crude Oil Prices During Covid-19 Pandemic

Implementation of Short-Term Forecasting Models in the Natural Gas Industry Financial Data

Future Natural Gas Price Forecasting Model and Its Policy Implication

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