Using High-Frequency Entropy to Forecast Bitcoin’s Daily Value at Risk

In this paper we investigate the ability of several econometrical models to forecast value at risk for a sample of daily time series of cryptocurrency returns. Using high frequency data for Bitcoin, we estimate the entropy of intraday distribution of logreturns through the symbolic time series analy...

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Detalles Bibliográficos
Autores principales: Pele, Daniel Traian, Mazurencu-Marinescu-Pele, Miruna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514585/
https://www.ncbi.nlm.nih.gov/pubmed/33266818
http://dx.doi.org/10.3390/e21020102

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514585/
https://www.ncbi.nlm.nih.gov/pubmed/33266818
http://dx.doi.org/10.3390/e21020102

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