Modeling bitcoin price volatility using the ARCH model

Authors

  • Bensaoucha Abdelkarim University of El-oued, Algeria
  • Rabia Bousbia Laiche University of El-oued, Algeria

Keywords:

Bitcoin, Volatility modelling, ARCH model

Abstract

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.

References

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Published

2024-10-25

How to Cite

Abdelkarim, B., & Laiche, R. B. (2024). Modeling bitcoin price volatility using the ARCH model. International Journal of Economic Perspectives, 18(10), 1827–1840. Retrieved from https://ijeponline.org/index.php/journal/article/view/684

Issue

Section

Peer Review Articles