Estimating quadratic variation using realized variance

Abstract This paper looks at some recent work on estimating quadratic variation using realized variance (RV)—that is, sums of M squared returns. This econometrics has been motivated by the advent of the common availability of high‐frequency financial return data. When the underlying process is a semimartingale we recall the fundamental result that RV is a consistent (as M → ∞) estimator of quadratic variation (QV). We express concern that without additional assumptions it seems difficult to give any measure of uncertainty of the RV in this context. The position dramatically changes when we work with a rather general SV model—which is a special case of the semimartingale model. Then QV is integrated variance…

• EA
  Economic and Social Research Council