Power Variation and Time Change

By Ole E. Barndorff-Nielsen, N. Shephard. 2006

57 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9a8bc2cdcb2e70a95bc2bdb5b496881087be6cda

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Citations in the corpus

Realized Volatility: A Review (2008, background): “Andersen, T. G., Bollerslev, T., Diebold, F. X. (2007).”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “Closer to the content of this paper, Barndorff-Nielsen and Shephard (2005b) study the first two moments of realised QV when Yt = Zτ t ,where τ is the integral of a non-negative covariance stationary process, that is a process with non-decreasing sample paths, and Z is a Lévy process.”
Power and bipower variation with stochastic volatility and jumps (2003, methodology): “Maheu and McCurdy (2003) have constructed a jump/GARCH discrete time model for daily data which attempts to unscramble jumps and volatility changes, while Eraker, Johannes, and Polson (2003) look at the same issue based on SV models using Markov chain Monte Carlo methods (using the methods of handli”
Irregular sampling and central limit theorems for power variations: The continuous case (2011, background): “An interesting – and crucial – feature of our result, as well as in the results of [1], is that the properties of the observation scheme are not showing explicitly in the result itself, and in particular the knowledge of the process a(2) and even of the rates rn is not necessary to apply it.”
Realized volatility with stochastic sampling (2010, methodology): “The asymptotic theory of the realized volatility and related statistics has been well-developed for the equidistant sampling case τ j = j/n with n → ∞; see Jacod and Protter [14], Barndorff-Nielsen and Shephard [2], and Barndorff-Nielsen et al. [1] among others.”
Estimation of integrated volatility in stochastic volatility models (2005, methodology): “Y (2) t satisfies (11) with a ∈ (3/4, 1) and the stochastic volatility component satisfies the same conditions as in Theorem 2.”
Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise (2004, methodology): “That the subsample estimator is closely related to the Bartlett kernel is perhaps not too surprising, because Bartlett (1950) motivated the Bartlett kernel with the subsampling idea, see also Anderson (1971, p.”