Jumps or flatness?

We show that flatness, that is the pervasive presence of zero returns in high-frequency data, is heavily detrimental for reliable jump inference. Even moderate levels of flatness, compatible with those observed in actual... [ view full abstract ]

We show that flatness, that is the pervasive presence of zero returns in high-frequency data, is heavily detrimental for reliable jump inference. Even moderate levels of flatness, compatible with those observed in actual prices, imply a large number of false positives when detecting jumps, and a sizable negative bias in the measurement of the jump activity index. We provide limit theorems for multipower variation under flat trading which allow to quantify the bias, and propose a simple recipe for its correction. We use the flatness-robust multipowers to reappraise the statistical features of jumps in empirical finance.