Forecasting of BTC volatility: comparative study between parametric and nonparametric models

Abstract

Bitcoin has rapidly gained much attention by media, investors and scholars, since it is widely used for investment purposes as an alternative to regular currencies. The price of bitcoin is characterized with high volatility, which makes firms that hold large amounts of it prone to risks. To prevent those risks and to gain insights into its behavior and trading strategies, it is necessary to forecast bitcoin volatility. In that context, this study reviews the impact of different factors on BTC price, returns and volatility. Moreover, it examines the sensitivity of GARCH family models to standardized residuals distributions in forecasting BTC volatility based on the leverage effect. Then, it compares the best GARCH-type model against the best ANN model with respect to short-term and long-term horizons. Results outline that APARCH, TGARCH and EGARCH are very sensitive to standardized residuals distributions, such that TGARCH run with the normal distribution is the best model that captures BTC volatility. Further, MLP outperforms all the parametric and nonparametric models, while its accuracy weakens with the level of forecasting horizons. Consequently, nonparametric models prevail parametric models in BTC volatility forecasting due to their high degree of flexibility and strong generalization abilities, whereas MLP is only effective in short-term forecasting.