Risk quantification and validation for Bitcoin
[EN] This paper introduces a semi-nonparametric approach for modeling Bitcoin risk relatively to other parametric distributions and volatility models. Model performance is assessed through different backtesting techniques, including multinomial test, for three risk measures: Value-at-Risk, Expected...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2020 |
| País: | España |
| Recursos: | Universidad de Salamanca (USAL) |
| Repositório: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/161229 |
| Acesso em linha: | http://hdl.handle.net/10366/161229 |
| Access Level: | Acesso embargado |
| Palavra-chave: | Cryptocurrencies Gram-Charlier Median shortfall Bacltesting GAS models Robust GARCH 5308 Economía General |
| Resumo: | [EN] This paper introduces a semi-nonparametric approach for modeling Bitcoin risk relatively to other parametric distributions and volatility models. Model performance is assessed through different backtesting techniques, including multinomial test, for three risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. Our results show that the ‘large’ semi-nonparametric expansion is a good alternative to measure Bitcoin risk according to recommendations of Basel Committee on Banking Supervision, but also that 99%-Median Shortfall seems to be an accurate and robust risk measure for Bitcoin. |
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