Crude oil price (COP) data are time-series data that are assessed as having both volatility and heteroscedasticity variance. One of the best models that can be applied to address the heteroscedasticity problem is GARCH (generalized autoregressive conditional heteroscedasticity) model. The purpose of this study is to construct the best-fitted model to forecast daily COP as well as to discuss the prepared recommendation for reducing the impact of daily COP movement. Daily COP data are observed for the last decade, i.e., from 2009 to 2018. The finding with the error of less than 0.0001 is AR (1) -GARCH (1,1). The implementation of the model is applicable for both predicting the next 90 days for the COP and its anticipated impact in the future. Because of the increasing prediction, it is recommended that policymakers convert energy use to renewable energy to reduce the cost of oil use.

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.

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.

Indonesia is currently one of the largest coffee producers in the world, and involved in exporting coffee countries. The financial series data such export value of coffee is highly volatile in both mean and variance. Thereby, the model of ARIMA with order p,d,q is one way to deal with this error. The aim of this study is to determine the best-fitted ARIMA(p,d,q) model to forecast the monthly series of export of coffee from January 2005 to April 2020. The findings suggest that ARIMA(1,3,1) is the best-selected model due to its very significant p-value (less than 0.0001), which showed that the model is applicable for forecasting.  The model is then used to establish the prediction of ExCof monthly data for the next 12 months.

low environmental management applied by the company amid the modern economic era now causes easy environmental damage. This condition must be overcome so that the company's image remains well respected and financial performance has an increasing income. Financial performance is influenced by several factors, including environmental performance, environmental costs, company size and disclosure of corporate social responsibility. This study aims to determine the effect of environmental performance, environmental costs and company size on financial performance with corporate social responsibility as the intervening variable. The population in this study are manufacturing companies listed on the Indonesia Stock Exchange in 2018 - 2020, with a total of 195 companies. Using the purposive sampling method, 38 companies meet the criteria with 114 data sets. The data were analyzed regressively using SmartPLS through descriptive statistical analysis tests, outer models, inner models and hypothesis testing. The results showed that environmental performance, environmental costs and corporate social responsibility had no effect on financial performance, but firm size had a significant positive effect on financial performance. Environmental performance has a significant positive effect on corporate social responsibility. In contrast, environmental costs and company size have no effect on corporate social responsibility, and corporate social responsibility is not proven and can become an intervening variable (mediation) on environmental performance, environmental costs, and company size on financial performance.