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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]

The occurrence of earthquakes is increasing almost every year in Indonesia. From January 2014 to December 2017, there was around 16,645 earthquakes with magnitude ≥4 Richter Scale occurred. This study is the first part of earthquake risk modeling that we conducted. This study aims to analyze the relationship of frequency and magnitude of the earthquakes by using Poisson Regression and Generalized Poisson Regression. The data used in this study is frequency and magnitude data of earthquakes occurred in Indonesia. The data were selected by selecting earthquakes with magnitude ≥4 Richter Scale in the period January 2014 to December 2017 (4 years). The dependent variable is frequency, meanwhile the magnitude is independent. The frequency of earthquakes is the rounded value of natural log (Ln) transformation of cumulative frequency of earthquakes occurred in time period, and tested that it follows poisson distribution. The Poisson Regression analysis was done for the first, then the analysis continued by using Generalized Poisson Regression to observe whether there is equidispersion effect. The result of two models was compared then continued by selecting the best model based on the smallest of Akaike Information Criterion (AIC). According to the result of Poisson Regression as well as Generalized Poisson Regression, the magnitude is significantly affect the frequency. Based on AIC, the best model of frequency-magnitude relationship is presented by Poisson Regression model, μ = exp (4.048 – 0.3935x). [ABSTRACT FROM AUTHOR]

Stock is one of the most popular investment instruments interested by investors. The main indicator for Indonesian stock market movement in Indonesia is called Jakarta Composite Index (JKSE) or Indonesia Stock Exchange (IDX). JKSE is a stock price index that is arranged and calculated with the results of trend movements, where the index can be used to compare events in the form of price changes from time to time. JKSE is calculated using the average method based on the prices of the shares listed in the index and divided by the number of shares listed in the index. Therefore, the JKSE can be modeled based on the prices of several listed shares, provided no multicollinearity. This study proposes the Principal Component Regression (PCR) for estimating the JKSE price index. We took five banking shares as the predictor, such as Bank Negara Indonesia (BBNI), Bank Central Asia (BBCA), Bank Rakyat Indonesia (BBRI), Bank Mandiri (BMRI), Bank Tabungan Negara (BBTN). Furthermore, the Value at Risk (VaR) and Expected Shortfall (ES) will be used to analyze the risk. Based on the 95 percent confidence level analysis, the VaR and ES values for JKSE are small, less than 5%. It means that JKSE has a relatively low-risk measure. Thus, investors can also assume that the shares used in this study are also classified as investment instruments that have low risk. [ABSTRACT FROM AUTHOR]