The Predictive Performance of Extreme Value Analysis Based-Models in Forecasting the Volatility of Cryptocurrencies

Abstract

This paper implements the analysis of volatility behaviour of the eight major cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Dash and Tether) for the period starting from October 13th 2015 to November 18th 2019. The GARCH-type models with heavy-tailed distributions are fitted to filter the conditional volatility exhibited by cryptocurrencies. Extreme value analysis based on the peak over threshold approach is then used to model the extreme tail behaviour of the cryptocurrencies. The predictive performance of the GARCH-EVT model in forecasting Value-at-Risk is evaluated at both 5% and 1% levels of significance. The backtesting results demonstrate the superiority of the GARCH-EVT model in both out-of-sample forecasts and goodness-of-fit properties to cryptocurrency returns and forecasting Value-at-Risk. Overall, the empirical results of this study recommend the heavy-tailed GARCH-EVT based model for modelling and forecasting the volatility of cryptocurrencies.