Limit distribution results on realized power variation, that is, sums of absolute powers of increments of a process, are derived for certain types of semimartingale with continuous local martingale component, in particular for a class of flexible stochastic volatility models. The theory covers,...

This article shows that realized power variation and its extension, realizedbipower variation, which we introduce here, are somewhat robust to rarejumps. We demonstrate that in special cases, realized bipower variation estimates integrated variance in stochastic volatility models, thus providing...

Estimating the covariance and correlation between assets using high frequency data is challenging due to market microstructure effects and Epps effects. In this paper we extend Xiu’s univariate QML approach to the multivariate case, carrying out inference as if the observations arise from an...

High frequency financial data allows us to learn more about volatility, volatility of volatility and jumps. One of the key techniques developed in the literature in recent years has been bipower variation and its multipower extension, which estimates time-varying volatility robustly to jumps. We...

When high-frequency data is available, in the context of a stochastic volatility model, realised absolute variation can estimate integrated spot volatility. A central limit theory enables us to do filtering and smoothing using model-based and model-free approaches in order to improve the...

This paper presents some limit theorems for certain functionals of moving averages of semimartingales plus noise, which are observed at high frequency. Our method generalizes the pre-averaging approach (see [13],[11]) and provides consistent estimates for various characteristics of general...