Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting

Beatriz Acereda, Angel Leon and Juan Mora

Finance Research Letters, 2020, vol. 33, issue C

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 (search for similar items in EconPapers)
JEL-codes: C22 C58 G1 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:33:y:2020:i:c:s1544612319300741

DOI: 10.1016/j.frl.2019.04.037

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