Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies.

Cheng, Jie

doi:10.1007/s00181-023-02360-7

Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies.

Cheng, Jie

Authors

Jie Cheng j.cheng@keele.ac.uk

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.

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