Jie Cheng j.cheng@keele.ac.uk
Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies.
Cheng, Jie
Authors
Abstract
In this paper, we investigate the co-dependence and portfolio value-at-risk of cryptocurrencies, with the Bitcoin, Ethereum, Litecoin and Ripple price series from January 2016 to December 2021, covering the crypto crash and pandemic period, using the generalized autoregressive score (GAS) model. We find evidence of strong dependence among the virtual currencies with a dynamic structure. The empirical analysis shows that the GAS model smoothly handles volatility and correlation changes, especially during more volatile periods in the markets. We perform a comprehensive comparison of out-of-sample probabilistic forecasts for a range of financial assets and backtests and the GAS model outperforms the classic DCC (dynamic conditional correlation) GARCH model and provides new insights into multivariate risk measures.
Citation
Cheng, J. (2023). Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies. Empirical Economics, 65(2), 899-924. https://doi.org/10.1007/s00181-023-02360-7
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 2, 2023 |
Online Publication Date | Jan 16, 2023 |
Publication Date | Aug 1, 2023 |
Journal | Empirical Economics |
Print ISSN | 0377-7332 |
Publisher | Springer Verlag |
Volume | 65 |
Issue | 2 |
Pages | 899-924 |
DOI | https://doi.org/10.1007/s00181-023-02360-7 |
Keywords | Portfolio management, Multivariate probabilistic forecasts, G17, Cryptocurrencies, C53, Generalized autoregressive score (GAS) model, G11 |
Publisher URL | https://link.springer.com/article/10.1007/s00181-023-02360-7 |
Files
Cryptocurrencies Empirical Economics.pdf
(4.9 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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