The Role of Jumps in Realized Volatility Modeling and Forecasting

Abstract Building on an extensive empirical analysis, I investigate the relevance of jumps and signed variations in predicting realized volatility. I show that properly accounting for intra-day volatility patterns and staleness sensibly reduces the identified jumps, in particular for low and moderate liquidity assets. Modeling realized variance using jumps and intra-day return sign improves the in-sample fit of commonly adopted specifications, irrespective of assets liquidity. From a forecasting perspective, the empirical evidence I report shows that most models, independently from their flexibility, are statistically equivalent in many cases. These results are confirmed with different samples, assets liquidity level, forecast horizons, and possible transformations of the dependent and explanatory variables.