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

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Title: Estimating the expected shortfall of cryptocurrencies: An evaluation based on backtesting
Authors: Acereda Serrano, Beatriz | León Valle, Ángel M. | Mora-López, Juan
Research Group/s: Finanzas de Mercado y Econometría Financiera | Economía Laboral y Econometría (ELYE)
Center, Department or Service: Universidad de Alicante. Departamento de Fundamentos del Análisis Económico
Keywords: Expected shortfall | Backtesting | Cryptocurrencies
Knowledge Area: Fundamentos del Análisis Económico | Economía Financiera y Contabilidad
Issue Date: Mar-2020
Publisher: Elsevier
Citation: Finance Research Letters. 2020, 33: 101181. doi:10.1016/
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.
Sponsor: Financial support from Spanish Ministerio de Economía, Industria y Competitividad (ECO2017-87069-P) is gratefully acknowledged.
ISSN: 1544-6123 (Print) | 1544-6131 (Online)
DOI: 10.1016/
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 Elsevier Inc.
Peer Review: si
Publisher version:
Appears in Collections:INV - ELYE - Artículos de Revistas
INV - Finanzas de Mercado y Econometría Financiera - Artículos de Revistas

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