We propose a new estimation scheme for estimation of the volatility parameters of a semimartingale with jumps based on a jump-detection filter. Our filter uses all of data to analyze the relative size of increments and to discriminate jumps more precisely. We construct quasi-maximum likelihood estimators and quasi-Bayesian estimators, and show limit theorems for them including $L^p$-estimates of the error and asymptotic mixed normality based on the framework of the quasi-likelihood analysis. The global jump filters do not need a restrictive condition for the distribution of the small jumps. By numerical simulation we show that our "global" method obtains better estimates of the volatility parameter than the previous "local" methods.

翻译：我们提出了一个新的估算计划,用于根据跳跃检测过滤器来估计带有跳跃的半边形参数的波动性参数。 我们的过滤器使用所有数据来分析递增的相对大小并更精确地区分跳跃。 我们构建了准最大可能性估测器和准巴伊西亚估测器, 并展示了这些参数的限值, 包括错误估计值, 以及基于准相似性分析框架的零星混合常态。 全球跳跃过滤器不需要对小跳跃的分布设置限制条件 。 通过数字模拟, 我们显示我们的“ 全球” 方法获得了比先前的“ 本地” 方法更好的波动参数估计值 。