 Volatility estimation through stochastic processes: Evidence from cryptocurrenciesMurad Harasheh and Ahmed Bouteska The North American Journal of Economics and Finance, 2025, vol. 75, issue PB Abstract: We apply stochastic volatility modeling enriched with leverage and an asymmetrically heavy-tailed distribution to analyze the returns of Bitcoin and Ethereum. Our methodology leverages the generalized hyperbolic skew Student’s t-distribution (GH-ASV-skw-st) framework, as proposed by Nakajima and Omori (2012), employing a Bayesian Markov chain Monte Carlo (MCMC) sampling technique for effectiveness evaluation. The GH-ASV-skw-st model is demonstrated to adeptly capture the stochastic volatility patterns present in the returns of cryptocurrencies. After validation with several diagnostics and robustness checks, we illustrate the model’s suitability for high-volatility series by capturing asymmetry, leverage effects, and tail risk. Our findings indicate that the model fits the data more precisely than traditional models and provides a more reliable foundation for risk measures essential to portfolio management, such as Value at Risk (VaR) and Expected Shortfall (ES). Keywords: Cryptocurrencies; Stochastic volatility; Skewed Student’s t-distribution with a generalized hyperbolic shape (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:75:y:2025:i:pb:s1062940824002456 DOI: 10.1016/j.najef.2024.102320 Access Statistics for this article The North American Journal of Economics and Finance is currently edited by Hamid Beladi More articles in The North American Journal of Economics and Finance from Elsevier |
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