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

【Author】 Mueller, Fernanda Maria; Santos, Samuel Solgon; Goessling, Thalles Weber; Righi, Marcelo Brutti

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【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

【发表时间】2022 AUG

【收录时间】2022-08-15

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-市场分析