The Role of Jumps in Realized Volatility Modeling and Forecasting
基于大量实证分析，研究了跳跃和符号变化在预测已实现波动率中的作用，发现考虑日内波动模式和滞后性会减少识别出的跳跃，且跳跃和日内收益符号能改善模型拟合，但多数预测模型在统计上等价。
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.