Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility
利用新发展的双幂变差测度和非参数跳跃检验，将波动率分解为跳跃和连续成分，发现跳跃成分重要但持久性低，分离后能显著提升样本外波动率预测，且许多跳跃与宏观经济新闻发布相关。
A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less persistent than the continuous component, and that separating the rough jump moves from the smooth continuous moves results in significant out-of-sample volatility forecast improvements. Moreover, many of the significant jumps are associated with specific macroeconomic news announcements. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.