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.
This paper examines tourists’ perceptions of Eastern Indonesia through comparisons with foreign visitors’ who have been to Indonesia, specifically Eastern Indonesia, and those who have never been to Indonesia. The aims of this study are to assess what foreign tourists perceive about Eastern Indonesia and elaborate the differences between the perceptions of visitors and non-visitors. The comparison of the visitor and non-visitor perceptions is important in order to understand Eastern Indonesia more deeply. This qualitative research uses focus group interviews to assess the perceptions of Eastern Indonesia. The perceptions from different participants came from different nationalities. In Group 1, two participants came from China, two Saudi Arabians, and one Bangladeshi. The four participants in Group 2 came from Australia. All of participants were students. The results indicate that tourists who have never been to Indonesia perceives Eastern Indonesia by comparing their experiences to other places that they have visited. From those experiences, they construct a positive image of Eastern Indonesia and as a result, indicate a willingness to visit Eastern Indonesia. All Australian participants had been to Indonesia and shared a positive image of Eastern Indonesia as well as of other places in Indonesia. However, while participants’ perceptions in Group 2 were positive, they also imply some ideas for the improvement regarding tourism development in Eastern Indonesia. Apart from that, all participants showed an intention to revisit and explore more places in EasternIndonesia in the near future.
Indonesia is one of largest users of sharia-based compliant recently which bring into many concerns how the sharia stocks listing in the most valuable sharia stocks index in Indonesia perform and correlate with other variables, particularly exchange rates. The study aims to analysis the causal relationship and to forecast the performances of sharia-based stocks and its Islamic index in Indonesia along with the volatility of exchange rate. Vector Autoregressive (VAR) model is applied as the method to analyse the multivariate time series as it is believed as the suitable model in predicting such time-series data in the scope of multivariate variables. The finding suggests VAR(1) model is the fitted model as such to both analyse its dynamic relationship and forecast the data set for the next 24 weeks. While the prediction shows the JII has an increasing data, both ANTM and EXR are predicted to have a stable volatility. In addition, granger causality defines variables to have effect in its respective variables, and IRF describes the shocks in one variable cause another variable is relatively difficult in reaching its zero condition in short-term period.
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