Volatility Analysis of Chili Price at Bengkulu Province Using Arima Approach Hariz Eko Wibowo1, Ridha Rizki Novanda 2, Muhammad Khaliqi3, Fika Harini Sinaga4, Dedy Darmansyah5, Achmad Amiruddin6, I.Rani Mellya Sari7
12Departement Of Socio-Economic Of Agriculture, Faculty Of Agriculture, Bengkulu university, Indonesia
3Agribusiness Department, Universitas Sumatera Utara, Indonesia
4Ministry of Agriculture, Indonesia
5Agribusiness Departement, Faculty Of Agriculture, Universitas of Teuku Umar
6Department of Agricultural Socio-economic, Faculty of Agriculture, Universitas Hasanuddin, Makassar 90245, South Sulawesi, Indonesia
7Agribusiness Department, Faculty of Agriculture, Lampung University, Indonesia
Abstract
Chili is an inflation-forming commodity (volatile food). Prediction of chili price time series data using the ARIMA model approach has good performance for predicting chili prices in the future. The second highest average per capita consumption of chili for a month is Bengkulu Province, below West Sumatra Province. This shows that the level of consumption and demand for chili is relatively high in Bengkulu Province. High demand has an impact on the price volatility of chili. The volatility analysis illustrates the standard deviation or diversity of chili prices that fluctuate over a certain period. Based on the description above, it is necessary to conduct research on the analysis of chili price volatility in Bengkulu Province. This research uses data sourced from the National Strategic Food Price Information Center. chili price volatility analysis using the ARIMA model with the help of R studio software. ARIMA models are suitable for time series data. The stages in this study were stationarity test, building the ARIMA model, selecting the best model, evaluating the model, identifying the ARCH effect and building the ARCH GARCH model, and calculating the volatility value. If there is no ARCH effect, there is no need to create an ARCH GARCH Model. the output of this research was the form of an international conference. The results of the data forecast using the ARIMA model (1,1,2) show prices that are relatively stable but have an upward trend. This shows that the level of volatility can be controlled and this is reinforced by the results that the model has no ARCH effect. The absence of an ARCH effect means that the data is still considered to have relatively the same diversity of variance and is in line with a relatively small volatility.
