Forecasting realized volatility of bitcoin returns : tail events and asymmetric loss

Abstract

We use intraday data to construct measures of the realized volatility of bitcoin returns. We then construct measures that focus exclusively on relatively large realizations of returns to assess the tail shape of the return distribution, and use the heterogeneous autoregressive realized volatility (HAR-RV) model to study whether these measures help to forecast subsequent realized volatility. We find that mainly forecasters suffering a higher loss in case of an underprediction of realized volatility (than in case of an overprediction of the same absolute size) benefit from using the tail measures as predictors of realized volatility, especially at a short and intermediate forecast horizon. This result is robust controlling for jumps and realized skewness and kurtosis, and it also applies to downside (bad) and upside (good) realized volatility.