The vector autoregressive moving average (VARMA) model is one of the statistical analyses frequently used in several studies of multivariate time series data in economy, finance, and business. It is used in numerous studies because of its simplicity. Moreover, the VARMA model can explain the dynamic behavior of the relationship among endogenous and exogenous variables or among endogenous variables. It can also explain the impact of a variable or a set of variables by means of the impulse response function and Granger causality. Furthermore, it can be used to predict and forecast time series data. In this study, we will discuss and develop the best model that describes the relationship between two vectors of time series data export of Coal and data export of Oil in Indonesia over the period 2002-2017. Some models will be applied to the data: VARMA (1,1), VARMA (2,1), VARMA (3,1), and VARMA (4,1). On the basis of the comparison of these models using information criteria AICC, HQC, AIC, and SBC, it was found that the best model is VARMA (2,1) with restriction on some parameters: AR2_1_2 = 0, AR2_2_1 = 0, and MA1_2_1 = 0. The dynamic behavior of the data is studied through Granger causality analysis. The forecasting of the series data is also presented for the next 12 months.
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.
Vector error-correction model (VECM) is a method of statistical analysis frequently used in many studies in time series data of economy, business and finance, and data energy. It is applied across researches due to its simplicity and limited restrictions. VECM can explain not only the dynamic behavior of the relationship among variables of endogenous and exogenous, but also among the endogenous variables. Moreover, it also explains the impact of a variable or a set of variables on others by means of impulse response function (IRF) and granger causality analysis. It can also be used for forecasting multivariate time series data. In this research, the relationship of three share price of energy (from three Asean countries: PGAS Malaysia, AKRA Indonesia, and PTT Thailand) will be studied. The data in this study were collected from October 2005 to August 2019. Based on the comparison of some VECM models, it was found that the best model is VECM (2) with cointegration rank = 3. The dynamic behavior of the data is studied through IRF, Granger Causality analysis and forecasting for the next five periods (weeks).
Mega Florist Bandar Lampung is a company engaged in the field of flower board rental and delivery services in the Bandar Lampung area, Mega Florist itself has been established since April 2015. The purpose of this research is to design an e-marketing system and implement an e-marketing system as a customer service at Mega Florist Bandar Lampung. The problem with Bandar Lampung's Mega Florist itself is that there is no system to market board flower rentals, and its marketing still uses brochures, print media, and advertisements so that it requires more costs to market board flower rental services to Mega Florist Bandar Lampung. The system development method uses the waterfall method and the system design uses the UML system design, and the system implementation uses an object-based programming language that is PHP, with Dreamwever and MySQL applications as a database. The result of this research is an online application based on e-marketing built by Mega Florist Bandar Lampung, focused on spreading information on package rental and flower delivery. This web board will present services, facilities, menus, promos and provide convenience for customers in finding information interest rates offered, as well as services in booking board interest rentals.
Time series analysis (time series) is one method with the aim to find out events that will occur in the future based on data and past circumstances. Time series are widely used in economics, business, environmental science, and finance. The analytical tool that is widely used to answer quantitative research problems is the Autoregressive Vector (VAR). The VAR model is used if the data is stationary. If the variable has cointegration and stationary at the first difference value, the VAR model is modified to become the Error Correction Model (VECM). Then we can find out the influence of variables with other variables by looking at the Impulse Response Function and Granger Causality. In this research, PT Kalbe Farma Tbk’s stock data will be analyzed. (KLBF) and PT Kimia Farma (Persero) Tbk (KAEF). The data used are weekly data from January 2010 to June 2020. Based on data analysis, it is known that the data is not stationary and there are unit roots. Furthermore, first differencing is done to make the data stationary. Because there was cointegration, a VECM analysis was performed and a VECM (p) was obtained with a lag of p = 4. So the best model for this research is VECM (4) with rank = 2. Causal relationships between variables using Granger Causality showed that KLBF influenced KAEF in the past. Based on IRF analysis, each variable gives a fluctuating response with itself and with other variables.
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