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A futures contract is a derivative product in the form of an agreement that requires parties to buy or sell an underlying asset at a defined price and time in the future, which can be used as a means of hedging, speculation, or arbitrage. In this study, we utilize Holt-Winter and Grey Holt-Winter methods to model the daily closing price of the United States (US) energy commodity futures. The best model will be selected based on the lowest Mean Absolute Percentage Error (MAPE). In addition, we use Value at Risk (VaR) to assess risk. We took four energy commodities futures in the United States (US) as a sample, such as Natural Gas, Gasoline RBOB, Heating Oil, and Crude Oil. We suppose that the data has a pattern consisting of trend and seasonality. The stationarity test is used to prove this, showing the rejection of the hypothesis. Based on the study, the best model for all futures commodities is Holt-Winter because it has a lower MAPE value than Grey Holt-Winter. This paper concludes that, based on VaR, the energy-futures with the lowest risk is Heating Oil while the highest risk is Natural Gas. [ABSTRACT FROM AUTHOR]

Investment in commodities has become an alternative investment that is increasingly in demand by the public in the last fifteen years. In commodity investment, there are two things that investors consider, namely return and risk. One way to calculate risk is to use Value at Risk (VaR) and Expected Shortfall (ES). The main reason of this research is to determine the value of Value at Risk (VaR) and Expected Shortfall (ES) of selected agriculture commodities which are Wheat, Cocoa and Cotton using the time series model approach. The data used in this research is the daily closing price of selected commodities from January 3, 2017 to December 31, 2020. In the time series modeling process, the models used for predicting commodities price movements are Autoregressive Integrated Moving Average (ARIMA) for the mean model, and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) for the volatility model. The values of mean and variance acquired from the model are then used to calculate the Value at Risk (VaR) and Expected Shortfall (ES) of each selected commodity. Based on the analysis, obtained that from the selected commodities, the estimated risk for selected commodities varies, where based on Value at Risk, Cotton has the lowest risk with a Value at Risk of 0.02189155, and Cocoa has the highest risk with a Value at Risk 0.02435271. However, Expected Shortfall gives a different conclusion, where Cocoa has the lowest risk with an Expected Shortfall value of 0.02435271 and Cotton has the highest risk with an Expected Shortfall value of 0.03114681. [ABSTRACT FROM AUTHOR]