A Comparison of Methods for Forecasting Value-at-Risk and Expected Shortfall of Cryptocurrencies

Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long-memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, as well as quantile regression-based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their Value-at-Risk and Expected Shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that includes periods of turbulence due to the COVID-19 pandemic, the third halving of Bitcoin and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for both cryptocurrencies, procedures taking into account the presence of extreme observations are preferable for forecasting both risk measures, outperforming long-memory models, regime-switching models, quantile regression-based models and combining strategies