Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies
Jie Cheng ()
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Jie Cheng: Keele University
Empirical Economics, 2023, vol. 65, issue 2, No 11, 899-924
Abstract:
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.
Keywords: Cryptocurrencies; Generalized autoregressive score (GAS) model; Multivariate probabilistic forecasts; Portfolio management (search for similar items in EconPapers)
JEL-codes: C53 G11 G17 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)
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DOI: 10.1007/s00181-023-02360-7
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