Acereda Serrano, Beatriz, León Valle, Ángel M., Mora-López, Juan
Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting
Finance Research Letters. 2020, 33: 101181. doi:10.1016/j.frl.2019.04.037
URI: http://hdl.handle.net/10045/106976
DOI: 10.1016/j.frl.2019.04.037
ISSN: 1544-6123 (Print)
Abstract: 
We estimate the Expected Shortfall (ES) of four major cryptocurrencies using various error distributions and GARCH-type models for conditional variance. Our aim is to examine which distributions perform better and to check what component of the specification plays a more important role in estimating ES. We evaluate the performance of the estimations using a rolling-window backtesting technique. Our results highlight the importance of estimating the ES of Bitcoin using a generalized GARCH model and a non-normal error distribution with at least two parameters. Though the results for other cryptocurrencies are less clear-cut, heavy-tailed distributions continue to outperform the normal distribution.
Keywords:Expected shortfall, Backtesting, Cryptocurrencies
Elsevier
info:eu-repo/semantics/article