Share price as one kind of financial data is the time series data that indicates the level of fluctuations and heterogeneous variances called heteroscedasticity. The method that can be used to overcome the effect of autoregressive conditional heteroscedasticity effect is the generalised form of ARCH (GARCH) model. This study aims to design the best model that can estimate the parameters, predict share price based on the best model and show its volatility. In addition, this paper discusses the prediction-based investment decision model. The findings indicate that the best model corresponding to the data is AR(4)-GARCH(1,1). The model is implemented to forecast the stock prices of Indika Energy Tbk, Indonesia, for 40 days and significantly presented good findings with an error percentage below the mean absolute.

The study examines the relationship between economic growth and life expectancy by considering the potential role of financial development and energy consumption in ASEAN Countries. Unit root testing was applied to check the level stationarity data before checking for cointegration between variables using the Error Correction Term (ECT) approach, and ARDL bounds testing was applied for cointegration with structural damage that occurred at a specific time using the Pooled Mean Group (PMG) and Pooled Mean Group (MG). The empirical results showed the existence of cointegration among variables. PMG was selected based on Hausmann Test that indicated energy consumption could significantly and positively affect life expectancy. Therefore, ASEAN countries would be extensively dependent on non-renewable energy to generate their economic activities in the long run. In contrast, in the short run, higher economic growth can reduce life expectancy in most developing countries, as energy consumption is examined to affect life.

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.

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.

This study discusses an interactive model that integrates behaviour theory with ethical theory to determine individual behaviour towards digital piracy. This study uses a quantitative approach by testing assumptions using the Structural Equation Model (SEM) assisted using the AMOS 4.0 application program. The results showed that the Theory of Planned Behavior (TPB) and the theory of marketing ethics (HV theory) could be used to predict the intention to commit digital piracy. Digital piracy intentions are not influenced by TPB's arbitrary rules, while digital piracy expectations and behaviour management significantly impact digital piracy intentions. Moral obligations and perceived benefits directly influence digital piracy. Moral obligation has clear negative effects, whereas perceived benefits positively impact piracy. Moral obligation hurts subjective value. Meanwhile, the perceived dangers often undermine individual attitudes towards digital piracy. The benefits people experience influence attitudes to digital piracy. This habit has had a dramatic and positive impact on digital piracy.