Autoregressive Integrated Moving Average (ARIMA) model for forecasting cryptocurrency exchange rate in high volatility environment: a new insight of bitcoin transaction

• Main Authors: Abu Bakar, Nashirah, Rosbi, Sofian
• Format: Article
• Language: English
• Published: 2017
• Subjects: HG Finance
• Online Access: http://repo.uum.edu.my/26299/1/IJAERS%204%2011%202017%20130%20-137.pdf; http://repo.uum.edu.my/26299/; http://doi.org/10.22161/ijaers.4.11.20
• Institution: Universiti Utara Malaysia

The cryptocurrency is a decentralized digital money. Bitcoin is a digital asset designed to work as a medium of exchange using cryptography to secure the transactions, to control the creation of additional units, and to verify the transfer of assets. The objective of this study is to forecast Bitcoin exchange rate in high volatility environment. Methodology implemented in this study is forecasting using autoregressive integrated moving average (ARIMA). This study performed autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis in determining the parameter of ARIMA model. Result shows the first difference of Bitcoin exchange rate is a stationary data series. The forecast model implemented in this study is ARIMA (2, 1, 2). This model shows the value of R-squared is 0.444432. This value indicates the model explains 44.44% from all the variability of the response data around its mean. The Akaike information criterion is 13.7805. This model is considered a model with good fitness. The error analysis between forecasting value and actual data was performed and mean absolute percentage error for ex-post forecasting is 5.36%. The findings of this study are important to predict the Bitcoin exchange rate in high volatility environment. This information will help investors to predict the future exchange rate of Bitcoin and in the same time volatility need to be monitor closely.This action will help investors to gain better profit and reduce loss in investment decision.