Forecasting realized volatility of Bitcoin: The informative role of price duration

Abstract

Motivated by the relationship between trading intensity and volatility and the attractiveness of duration‐based volatility estimators, this paper investigates the ability of price duration to forecast realized volatility of Bitcoin. Using high‐frequency transaction data, trading intensity is measured by price duration and incorporated in the class of heterogeneous autoregressive (HAR) models. Results provide compelling evidence that trading intensity improves the forecasting performance of a highly competitive set of HAR models, commonly used in the literature. HAR extensions that incorporate price duration systematically deliver the lowest forecast errors and generate economically significant gains in volatility targeting exercise over multiple horizons. However, results show no evidence in favor of a unique duration‐augmented model. The predictive ability of price duration is supported by a number of robustness checks, including alternative estimation windows, bull and bear market states, and alternative thresholds that define price events.