 Robust estimation of the range-based GARCH model: Forecasting volatility, value at risk and expected shortfall of cryptocurrenciesPiotr Fiszeder (), Marta Małecka and Peter Molnár Economic Modelling, 2024, vol. 141, issue C Abstract: Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator, which combines these two improvements with a third new improvement: a modified robust method, which adds elasticity in treating the outliers. We apply this model to Bitcoin, Ethereum Classic, Ethereum, and Litecoin and find that it forecasts variances, value at risk, and expected shortfall more accurately than the standard GARCH model, the standard range-based GARCH model, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail. Keywords: Cryptocurrency; Bitcoin; Volatility models; Value at risk; Expected shortfall; High-low range; Robust estimation; Outliers (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:ecmode:v:141:y:2024:i:c:s026499932400244x DOI: 10.1016/j.econmod.2024.106887 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
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