Measuring value-at-risk and expected shortfall of newer cryptocurrencies: new insights

A significant amount of historical returns is needed for the generalized autoregressive conditional heteroscedasticity (GARCH) models to be calibrated. Newer cryptocurrencies, such as non-fungible tokens (NFTs), have relatively limited data to create robust parameter estimates. This study uses a new...

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Bibliographic Details
Published in:Cogent business & management 2024-12, Vol.11 (1)
Main Authors: Mappadang, Agoestina, Nugroho, Bayu Adi, Lestari, Setyani Dwi, Elizabeth, Lestari, Titi Kanti
Format: Article
Language:English
Subjects:
Accuracy
business, management and Accounting
C46
Digital currencies
EVT
Expected shortfall
exponentially-weighted moving average
Finance
financial Accounting
GARCH
Literature reviews
Non-fungible tokens
Normal distribution
Portfolio management
Regulatory approval
Stochastic models
value-at-risk
Citations: Items that this one cites
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Summary:A significant amount of historical returns is needed for the generalized autoregressive conditional heteroscedasticity (GARCH) models to be calibrated. Newer cryptocurrencies, such as non-fungible tokens (NFTs), have relatively limited data to create robust parameter estimates. This study uses a newly developed method, the exponentially weighted moving average (EWMA) model, that takes into account the fat-tailed distributions of returns and volatility response to forecast Value-at-Risk (VaR) and Expected Shortfall (ES). We employ thorough back tests of daily VaR and ES forecasts, which are widely utilized for regulatory approval and are considered to be industry standards. We also use loss function ratios to select the best model. Our results indicate that simpler models are just as good as the complicated ones, provided the simpler models capture fat-tailed distributions of returns. The primary findings hold up through several tests.
ISSN:2331-1975
2331-1975
DOI:10.1080/23311975.2024.2416096