  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.
