Ole E. Barndorff-Nielsen

TL;DR: In this paper, the moments and the asymptotic distribution of the realized volatility error were derived under the assumption of a rather general stochastic volatility model, and the difference between realized volatility and the discretized integrated volatility (which is called actual volatility) were estimated.

TL;DR: The authors construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive Ornstein-Uhlenbeck (OU) processes, and study these models in relation to financial data and theory.

TL;DR: Barndorff-Nielsen and Shephard as mentioned in this paper showed that realized power variation and its extension, realized bipower variation, which they introduce here, are somewhat robust to rare jumps.

TL;DR: A number of stochastic processes with normal inverse Gaussian marginals and various types of dependence structures are discussed, including Ornstein-Uhlenbeck type processes, superpositions of such processes and Stochastic volatility models in one and more dimensions.

TL;DR: In this article, realised kernels are used to carry out efficient feasible inference on the expost variation of underlying equity prices in the presence of simple models of market frictions, where the weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which is close to that of the maximum likelihood estimator in the parametric version of this problem.