We investigate the possibility of forecasting market illiquidity and asses the role of conditional jumps in determining the distributional properties of the realized Amihud with the goal of introducing the concept of Illiquidity-at-Risk (IlliquaR). We show that the inclusion of jumps drastically increases the forecasting capability of the models at least in a short horizon. Finally, accounting for illiquiity jumps allows for a correct probability coverage of extreme illiquidity events.