Journal
FINANCE RESEARCH LETTERS
Volume 33, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.frl.2019.04.037
Keywords
Expected shortfall; Backtesting; Cryptocurrencies
Categories
Funding
- Spanish Ministerio de Economia, Industria y Competitividad [ECO2017-87069-P]
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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 rollingwindow 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.
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