Forecasting and backtesting systemic risk in the cryptocurrency market

Sheng Fang, Cao Guangxi and Paul Egan

Finance Research Letters, 2023, vol. 54, issue C

Abstract: Cryptocurrency has become an increasingly important tool in both portfolio investment and government regulation. As a relatively new asset class, cryptocurrencies are prone to extreme volatility, with the potential for significant downward movements over the short term. This paper uses MES and △CoVaR to forecast the systemic risk in the cryptocurrency market and subsequently tests the validity based on unconditional coverage and independence. The results of this paper show that a DCC-GARCH model performs well in forecasting systemic risk. The paper also shows that Aoen, EOS and Sinacoin are the best forecasters of systemic risk across the 191 cryptocurrencies analysed over the full estimation period. Our findings have important implications for investors and policy-makers with a vested interest in the cryptocurrency market.

Keywords: Cryptocurrency; Systemic risk; Forecasting; Backtesting (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323001617

DOI: 10.1016/j.frl.2023.103788

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