This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high‐frequency data on four prominent energy markets, we perform a model‐free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in‐sample and out‐of‐sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons, and loss functions. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:758–792, 2016