Modeling bitcoin price volatility using the ARCH model
Keywords:
Bitcoin, Volatility modelling, ARCH modelAbstract
This study aims to identify volatility models for the daily closing price of Bitcoin in USD during the period from January 1, 2020, to June 30, 2024, by applying autoregressive conditional heteroskedasticity (ARCH) models, where the error distribution follows the normal distribution. These models take into account price fluctuations during the trading period. The results indicate that the best model for estimating the time series data of the daily closing price of Bitcoin is the EGARCH model, among other ARCH models, as it has the lowest value for the statistical criteria used (H-Q, SIC, and AIC) for model selection. This confirms the importance of using ARCH models in volatility (risk) analysis, leading to accurate and reliable conclusions that benefit market participants. Additionally, the results show the presence of variance effects on the time series of daily closing prices, which was confirmed by the ARCH test on residuals. This implies that there are fluctuations in the daily closing prices of Bitcoin, necessitating the use of ARCH family models to predict daily Bitcoin closing prices.
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Copyright (c) 2024 Bensaoucha Abdelkarim, Rabia Bousbia Laiche

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