Cryptocurrency Modeling and Price Prediction Using Markov Switching Autoregressive Model - David Kaluge; Patricia Juan Aurellia (Universitas Negeri Semarang, 2024)
The emergence of cryptocurrency investment trends has brought the number of registered customers for crypto assets in Indonesia to surpass the number of investors in the capital market. Despite the continuous increase in the number of cryptocurrency investors, a different scenario is depicted by the declining transaction values. This decrease is attributed to the high volatile nature of cryptocurrency coins, which impacts investors’ investment decisions. This research aims to obtain the best model and forecasts related to cryptocurrency prices in order to minimize concerns and potential losses experienced by investors. This research uses the closing price of the five largest market capitalized cryptocurrency coins. The research utilized the Markov Switching Autoregressive method to capture structural changes in the data, allowing it to be used for forecasting. The research findings indicate that the best model for BTC is MS(3)AR(1), the model for BNB is MS(3)AR(1), the model for ETH is MS(3)AR(1), the model for XRP is MS(3)AR(2), and the model for ADA is MS(3)AR(2). The RMSE values indicate that BTC is the coin with the most accurate price prediction compared to other coins.