<![CDATA[The popularity of Bitcoin increased significantly in 2021. Bitcoin is considered to deliver high returns in a relatively short period, indicating that bitcoin has high volatility. Data with high volatility usually violates the Autoregresstive IntegratedinMovinginAverage (ARIMA)in homoscedasticity assumption. The Autoregressive Conditional Heteroscedasticity (ARCH) and General Autoregressive Conditional Heteroscedasticity (GARCH) model is often used to overcome the problem of heteroscedasticity in thelARIMA model. The ARCH and GARCH models canfbe used to model thefvolatilityfof data. This Research uses ARCH and GARCH models to overcome the heteroscedasticity problem caused by the high volatility of Bitcoin data for the period 30th June 2018 to 30th June 2022. The results of this study suggest that there might be a heteroscedasticity problem in Bitcoin data. The bestffiimodel for Bitcoin data ismiARIMA(1,0,[4])-GARCH(1,1) with an AIC value of -1,4263 at a 95% confidence level]]>
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