Recent research has focused on modeling asset prices by
Itô semimartingales. In such a modeling framework,
the quadratic variation consists of a continuous and
a jump component. This paper is about inference on
the jump part of the quadratic variation, which can
be estimated by the difference of realized variance
and realized multipower variation. The main
contribution of this paper is twofold. First, it
provides a bivariate asymptotic limit theory for
realized variance and realized multipower variation
in the presence of jumps. Second, this paper
presents new, consistent estimators for the jump
part of the asymptotic variance of the estimation
bias. Eventually, this leads to a feasible
asymptotic theory that is applicable in practice.
Finally, Monte Carlo studies reveal a good finite
sample performance of the proposed feasible limit
theory.