Comparison of risk forecasts for cryptocurrencies: A focus on Range Value at Risk

Fernanda Maria Müller, Samuel Solgon Santos, Thalles Weber Gössling and Marcelo Righi ()

Finance Research Letters, 2022, vol. 48, issue C

Abstract: We forecast the Range Value at Risk (RVaR) of main cryptocurrencies using the GARCH model with different error distributions. We compare the performance of the different forecasts using a score function. The normal and asymmetric normal distributions presented the best performance for RVaR. Our findings suggest that the main driver for the RVaR of cryptocurrencies is the conditional standard deviation and not the distribution of the stochastic term. For the Value at Risk (VaR) and Expected Shortfall (ES), non-normal distributions present the best performance. We also note the advantages of RVaR over ES regarding regulatory arbitrage and model misspecification.

Keywords: Range Value at Risk (RVaR); Cryptocurrencies; Bitcoin; Risk forecasting (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001878

DOI: 10.1016/j.frl.2022.102916

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