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.

This study aims to analyze company characteristics as a determinant of conventional and Islamic bank earnings management in several ASEAN countries (Association of South East Asian Nations). The Multiple Discriminant Analysis was applied to determine the differences between Islamic and Conventional Banks. This test was conducted based on Capital Adequacy Ratio, Income Before Tax and Interest, Non-Performing and Changing Loans, and Company's Size in the banks of Indonesia, Malaysia, and Brunei Darussalam from 2014 to 2018. The data obtained from 200 banking entities were analyzed discriminatively. The results showed that there were simultaneous differences between Capital Adequacy Ratio, Earnings Before Tax, Loan Loss Provision, Non-Performing and Changing Loans, and Company's Size as determinants of earnings management between Islamic and conventional banks. Also, it was found that Company's Size was the dominant variable determining the management differences. Based on Discriminant Analysis, there were significant differences in the determinants of conventional and Islamic earnings management. The Changing Loan variable showed the highest contribution in determining earnings management in Islamic banks. Overall, this study found that conventional banks dominated Islamic system in practicing earnings management.

This study points to increase global monetary integration as a result of rising volatility spillovers. As a result, analyzing volatility spillovers for international areas that expand and improve through the usage of inventory returns and oil prices is critical. The EGARCH model is used to explore the Volatility Spillovers of oil agencies in five ASEAN international areas during the Covid-19 Pandemic. To assess the interrelationships of the ASEAN stock index as well as the path of volatility, data were acquired from five global sites with very large volatility spillovers, namely Indonesia, Malaysia, Singapore, Thailand, and Vietnam. The findings show that this search is critical for ASEAN traders as well as Filipinos. Furthermore, because accurate forecasting of volatility spillover in global equity markets is required to reduce portfolio risk, this search has a substantial and viable significance.

Stock price data at State Gas Company is defined as the time-series data comprising varying volatility and heteroscedasticity. One of the best models used to solve the problem of heteroscedasticity is the GARCH (generalized autoregressive conditional heteroscedasticity) model. Therefore, this study aims to build the most suitable model for predicting the 186 days before and 176 days after the Covid-19 pandemic, as well as to provide recommendations to reduce the impact of daily stock price movements. Data were obtained by examining the daily stock price data in Indonesian National Gas Companies from 2019 to 2020. The study also discusses the Event Window, with the best model identified as AR (1) -GARCH (1,1). The result showed that an error of less than 0.0015 is AR (1) -GARCH (1,1), provided the best model for price forecasting of Indonesian National Gas Companies.

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