Risk measurement model on top 10 cryptocurrency market capitalization

Umar Al Faruq (), Dwi Fitrizal Salim () and Farida Titik Kristanti ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 4, 2395-2404

Abstract: This study conducted a large-scale analysis to evaluate the performance of traditional and Markov-Switching GARCH (MS-GARCH) models to estimate the volatility of the top 10 cryptocurrencies by market capitalization. The study compared the performance of the models using goodness-of-fit measures, specifically the Deviance Information Criterion (DIC) and the Bayesian Predictive Information Criterion (BPC). Secondly, we assess the forecasting accuracy for one-day-ahead conditional volatility and Value-at-Risk (VaR). The results obtained show that, in a manner consistent with the findings for the broader cryptocurrency market, the time-varying regime-switching model exhibits superior performance in capturing the complex volatility patterns observed in cryptocurrencies when compared to the traditional GARCH model.

Keywords: Cryptocurrency; E-GARCH; GARCH; T-GARCH; MSGARCH; Volatility. (search for similar items in EconPapers)
Date: 2025
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