Robust estimation of integrated variance and quarticity under flat price and no trading bias

This paper investigates a selection of methods disentangling contributions from price jumps to realized variance. Flat prices (consecutively sampled prices in calendar time with the same value) and no trading (no price observation at sampling points), both frequently occurring stylized facts in financial high-frequency datasets, can cause a considerable bias in each considered method. Hence, we propose an approach to robustify those methods so that they can provide undistorted statistical results based on intraday intervals not influenced by flat prices and no trading. The new approach is tested in realistic Monte Carlo experiments and shows to be extraordinary robust against varying levels of flat price and no trading bias. Additionally, we examine the new approach empirically with a dataset of electricity forward contracts traded on the Nord Pool Energy Exchange. We obtain coherent conclusions with respect to predefined qualitative indicators.